iiinova — where your digital self keeps evolving

i think that if you need to rely on an LLM to write your argument you don't actually have an argument.

Reposted by Ty

i suppose i’m not surprised that it is difficult to make people care about something that is low visibility — the vast majority of people have no idea who piker is — and low salience. i think we’re also going to find, for similar reasons, that the anti-trans messaging isn’t working either.

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Ty @tyanna.bsky.social · 30/09/2026

Dartmouth endorsing Pangram is a fascinating political move.

Guidelines on Use of Al Detection in Teaching
Adopted September 2026
These guidelines are formulated by the Office of the Dean of Faculty. Al detection is a fast-moving technology and, consequently, there may be reason to revise these guidelines in the future. The Office of the DOF will monitor developments in Al detection technology and will draw on ongoing consultation with: the Dartmouth Center for the Advancement of Learning (DAL), Learning Design and Innovation (LDI), Committee on Standards (COS), and the Committee on Organization and Policy (COP).
For the purposes of these guidelines, "faculty member" refers to the instructor of record. Only the instructor of record may submit student work for review under these guidelines or to the COS.
Guidelines
1. Faculty members may submit student writing assignments to Al detection software for the purpose of detecting possible Al use.
2. As of September 2026, Pangram is authorized for this purpose because it has shown the most reliability of available detectors.
3. Faculty members must inform students in writing of course policies governing permitted and prohibited uses of Al. Faculty members must also inform students in writing if student work may be submitted to Al detection software, and if results of such assessments could affect grades
4. Faculty members must remove all personally identifying information from student work before submitting it to Al detection software.'
5. Faculty members may not use the results of an assessment from Al detection software as a basis for penalizing or otherwise affecting a student's grade without first communicating with the student about the quality and integrity of their work.
6. Faculty members should be aware that, when hearing cases around Al tool use, the COS looks for a constellation of data (that is, information beyond the result of an Al-detection tool) to reach a finding of responsibility.
7. Faculty should be aware that Dartmouth is evaluating whether to purchase an instit…

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Linguistic Society of America @lingsocam.bsky.social · 29/09/2026

Big news: LSA membership is getting more equitable. October 1, dues will be income-based -- so your capacity to pay determines what you pay. More linguists, more voices, more community – no questions asked. Join or renew today! www.lsadc.org/membership   #LinguisticsCommunity #LSA #MembershipEquity

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Ty @tyanna.bsky.social · 05/09/2026

Linguists! What are some sociophonetic variables typical among contemporary adolescence at extremely elite cosmopolitan boarding school-type institutions? Iconic New England WASP schools if we have to narrow.

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Ty @tyanna.bsky.social · 26/08/2026

Absolutely horrified to find newborn voles in my bike pannier this afternoon 😬

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Ty @tyanna.bsky.social · 26/08/2026

I hit 1000 miles on the e-bike yesterday. Here’s a view 🌼

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Ty @tyanna.bsky.social · 16/08/2026

Dicksissle spotting in Kansas We also saw prairie chickens!

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Louis Römer @lromeranth.bsky.social · 13/08/2026

The real question is why did those who regard themselves as the reasonable ones go along with letting the fear of left authoritarianism become the justification for right-wing authoritarian action.

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Ty @tyanna.bsky.social · 13/08/2026

Great Basin National Park yesterday, Arches today ☀️

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Ty @tyanna.bsky.social · 05/08/2026

Marin County Civic Center

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Mark Thornton @markthornton.bsky.social · 03/08/2026

Very excited to share this preprint from me, @emmatempleton.bsky.social, @lindseytepfer.bsky.social & @thaliawheatley.bsky.social! "Friends and strangers engage in distinct regimes of body motion synchrony during conversation" osf.io/preprints/ps...

Forms of body motion synchrony. A) Motion quantity synchrony – synchrony in the amount of motion (orange) people engage in over time. This form of synchrony is agnostic to the direction of this movement, may be positive or negative, occurs at varying time lags, and is not specific to matched body parts across members of the dyad. B) Pseudo-synchrony – motion quantity synchrony between people who are not actually interacting. That is, members of different conversations engaging in similar amounts of motion at similar points in the conversation relative to its initiation. And C) directional synchrony – people moving the same body parts in the same direction (or along the same axis in the opposite direction) at the same time (or at some specific time lag).

Mechanisms of synchrony. A) Turn-taking, which causes increases in motion around the time of speaker role exchanges, as participants gesture to hand over or take over the floor. B) Conservation of motion in which one participant moves less when the other moves more, holding the total motion quantity relatively constant over time (note that the total amount of motion across the dyad is similar in the two panels, because one person reduces their motion to balance out the increase in the other’s motion). C) Entrainment, in which mutual following of the same conversational script leads to synchronized movement among people who are not directly interacting (i.e. pseudo-synchrony). Greeting gestures such as the handshakes depicted are one acute manifestation of this entrainment. D) Station-keeping, which maintains proxemic distance and eye level between members of the dyad. This mechanism accounts for directional synchrony, particularly between strangers who feel uncomfortable within each other’s personal space.

Motion quantity synchrony among friends and strangers. A) Friends displayed significant positive synchrony in total motion quantity at short time lags (< 2.5 s) and B) this effect generalized across the majority of body part pairs (each line represents one pair). For example, one series of points might represent the synchrony between the amount of motion in one participant’s left hand and the other’s right foot. C) Strangers displayed significant positive synchrony in total motion quantity at short time lags (< 1 s) and significant negative total motion quantity synchrony at longer time lags (2.5 – 16.6 s). D) Again, this effect generalized to most body part pairs.

Directional synchrony among friends and strangers. A) Among friends, we observed statistically significant directional synchrony in only two body parts (nose and eye) and only in the vertical axis. B) Among strangers, we observed statistically significant directional synchrony in a large number of body parts in the horizontal, vertical, and depth axes.

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Ty @tyanna.bsky.social · 16/07/2026

After weeks of tormenting the baby chickens, Kiwi has decided that she wants to be a mother and she’s taken her business outside of the coop and become broody. Updates via @markthornton.bsky.social (I’m in Chile)

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Ty @tyanna.bsky.social · 08/07/2026

I miss before AI when students used to write about (some variation of) “Since the beginning of time” all the time that was so fun

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Ty @tyanna.bsky.social · 24/06/2026

Today was the little girls’* first day out in public (the garden). The big girls were huffy and puffy about it. *one is a tiny baby rooster

Two big red chickens in a field of flowers

Two big barred rock chickens drinking water together

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Ty @tyanna.bsky.social · 21/06/2026

Frolicking in the garden 🐓🐓🐓🐓

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Ty @tyanna.bsky.social · 15/06/2026

students are very creative when they're not using AI. i gave someone podesva (2007) and eckert (2008) and he talked himself into a whole theory of (political) language socialization vis-a-vis a rational economy of indexical value.

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Ty @tyanna.bsky.social · 08/06/2026

Moved the monstera so that the cats can follow the chickens’ journey to and from the front yard more closely 🐓🐓🐓🐓

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Ty @tyanna.bsky.social · 06/06/2026

Asparagus, looking very bird 🐥

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Ty @tyanna.bsky.social · 03/06/2026

I've learned all there is to know about masculinities AMA.

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Ty @tyanna.bsky.social · 27/05/2026

For a transcription assignment, a student organized her data (line numbers, speaker, text) into cells in a table & then hid the lines of the boxes & I pointed out to everyone in class today that this is a good idea because look how organized her lines are and then we were all like 🤯🤯🤯 women in STEM

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Ty @tyanna.bsky.social · 25/05/2026

There are only two genders: tenure track and postdoc

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Ty @tyanna.bsky.social · 20/05/2026

three of my students this quarter are doing their ethnographic language and politics projects on their fraternities and I'm learning so much about masculinities.

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Jessi Grieser @jessgrieser.com · 20/05/2026

One of my grad students accurately summarizing the value proposition of AI assistants: “Give me access to everything and I might be able to help with something.”

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Ty @tyanna.bsky.social · 17/05/2026

Carrot, Peach, Crocus, Goji Berry, Tulip, Asparagus, and Primrose in the garden 🐣 Kiwi, Eki, Barb, and Squash staying far away 🐓🐓🐓🐓

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Ty @tyanna.bsky.social · 14/05/2026

People need to stop telling students it’s cheating to use AI to write their papers but it’s ok—even encouraged—to use LLMs to brainstorm/organize their ideas. Having your own ideas and organizing them is literally the point. Vibe writing kills ideas.

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Ty @tyanna.bsky.social · 11/05/2026

Attempted introductions, but the big chickens did not care.

Chicks in foreground, hens closer but ignoring them

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Ty @tyanna.bsky.social · 08/05/2026

Important chicken update: there are 7 more! Tulip, Asparagus, Peach, Primrose, Goji Berry, Crocus, and Carrot

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Nick Fleisher @nickfleisher.bsky.social · 07/05/2026

Canvas, the Strait of Hormuz for American education

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Ty @tyanna.bsky.social · 08/05/2026

They’re always hacking grades and medical information but never hacking student loan debt smh

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Ty @tyanna.bsky.social · 03/05/2026

Big fan of the sugar shack owner/guide who guided us in English but gave us commands only in French 🍁

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Ty @tyanna.bsky.social · 28/04/2026

gifts for friends🥚

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Ty @tyanna.bsky.social · 24/04/2026

My AI agent, Empanada.

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Ty @tyanna.bsky.social · 03/04/2026

Astronauts must be so good at sleeping. That has to be something they select for. I wouldn’t get any sleep because I’d be like “OMG I’M IN SPACE”, which can’t be good for doing science.

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Ty @tyanna.bsky.social · 28/03/2026

Anna’s Hummingbird in Napa Valley, CA.

Hummingbird behind a bunch of yellow flowers

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Ty @tyanna.bsky.social · 27/03/2026

Don’t use AI as a therapist to work through your relationship problems.

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Ty @tyanna.bsky.social · 22/03/2026

Someone laid their first egg! We don’t know who. Or why she chose to leave it teetering on a ledge. Still proud though.

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BART @bart.gov · 19/02/2026

Congrats to BART rider and Oakland legend Alysa Liu on winning a gold medal at the Olympics and making the Bay Area proud!

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Simon Fisher @profsimonfisher.bsky.social · 19/02/2026

“Humans across multiple languages spontaneously associate the nonwords kiki & bouba with spiky & round shapes, respectively...We tested the bouba-kiki effect in baby chickens. Similar to humans, they spontaneously chose a spiky shape when hearing a kiki sound & a round shape when hearing a bouba.”😲🧪

science.org

Matching sounds to shapes: Evidence of the bouba-kiki effect in naïve baby chicks

Humans across multiple languages spontaneously associate the nonwords “kiki” and “bouba” with spiky and round shapes, respectively, a phenomenon named the bouba-kiki effect. To explore the origin of t...

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Ty @tyanna.bsky.social · 15/02/2026

Four mile skate loop on Lake Morey. No falls this time! ⛸️

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Ty @tyanna.bsky.social · 14/02/2026

We always recognize Kiwi because she goes out of her way to make eye contact 👁️

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Reposted by Ty

Mark Thornton @markthornton.bsky.social · 11/02/2026

New preprint from Lindsey Tepfer (@ltjaql.bsky.social) and me! We silenced portions of internal monologues in two films to manipulate participants' access to characters' thoughts. Using ISC and RSA, we found that this aligned later neural processing of the narrative & encoding of trait impressions.

Figure 1. Experimental stimuli and paradigm. 1A. Manipulation timeline. Each video features a total of 12 IM moments, half of which were silenced in version 1, and the other half silenced in version 2. This produces two versions of each video where only half of the IMs are audible to the participant. 1B. Voice removal. Background sounds were preserved in both versions, regardless of IM presence. 1C. fMRI paradigm. Participants were randomly assigned a version and watched both videos in a counter-balanced order. 1D. Afterwards, they made trait ratings on each video’s main character in random order before rating them on subsequent traits.

Figure 2. Scanner and online participant trait rating results. A. Scanner participants arrive at similar conclusions about characters across versions by the end of each video. B. Trait ratings at the end of the videos are correlated between online and scanner participants. C. The IM manipulation had a significant effect on the trait ratings across the duration of the videos, such that different versions led to different trait impressions. D. Participants who saw different versions changed their ratings for both clip types but changed to a greater degree after seeing the IM segments relative to the NIM.

Figure 3. ISC results. Gaining access to the same mental state knowledge in earlier IM clips led to significantly (p < .05, corrected) more aligned neural activity in subsequent NIM clips across a wide swath of the temporal lobe, as well as the frontal pole and SPL.

Figure 4. Representational similarity analysis results. 4A. RSA pipeline. Clip-specific patterns of brain activity were correlated across movies within segment type (NIM or IM) to create a correlation distance matrix for each participant. Corresponding trait ratings were partitioned into components specific to each of the two versions of each film or shared across both versions. RDMs based on those traits were used to predict the neural RDMs. The resulting coefficients were sorted into matched (version), unmatched (version), and shared across version, and inference was performed via t-tests across participants. See methods section for further details of this analysis. 4B. Prior trait rating results. 1. While watching IM clips of the same version, the prior trait ratings predict activity in the bilateral A1, STS, STG, and right VLPFC. 2. Prior trait rating predicts activity in these regions again in the same-version NIM clips. 3. Across the two versions, while watching the NIM clips, the prior trait ratings predicted activity in the right occipitotemporal cortex. 4C. Trait updating results. 1. While watching NIM clips, the right STS & MTG predict version-matched trait updating. 2. In the unmatched NIM clips, STS & MTG again predict trait updating. 3. Across the two versions, the precuneus and LOC show pattern similarity for trait updating in participants.

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Ty @tyanna.bsky.social · 01/02/2026

Big fan of Vermont’s first cat cafe that’s also a tattoo parlor.

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