MétaCan
Menu
Back to cohort
Record W4413161140 · doi:10.24908/ijesjp.v12i1.19539

Do it differently! But how?

2025· article· en· W4413161140 on OpenAlexvenueno aff
Katerina Pia Günter, Elena Vasiliou

Bibliographic record

VenueInternational Journal of Engineering Social Justice and Peace · 2025
Typearticle
Languageen
FieldComputer Science
TopicDigital Education and Society
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Another university is possible! An exclamation point. A claim. A theme. The title of the 2024 ESJP conference. Do it differently! A call to reimagine conference engagements and practices. During the conference, I listened to colleagues presenting their work. Some presentations. But also workshops. An attempt to do academia differently. Another academia. Some of it different. More play. Less performance. More activity. Less lecturing. More colorful wax pastels and playdough. Less black words on white paper and screens. Some of it giving hope. Inspiring. Some of it challenging academic rules. Some of it playing academic tunes. Using academia’s colors. Performing academic performances. Puzzling. Why is it so hard to do it differently? Even if the invitation is to do it differently. Think. Feel. Believe. Another university possible? Another engineering possible? Maybe. But why hasn’t this been easier? Possible, so far? bell hooks writes, “Dominator culture has tried to keep us all afraid, to make us choose safety instead of risk, sameness instead of diversity. Moving through that fear, finding out what connects us, revelling in our differences; this is the process that brings us closer, that gives us a world of shared values, of meaningful community” (2003, p.197). How can we build these meaningful communities? And can we? This piece unpacks some thoughts on community in conversation. A collective movement from a caricature to regeneration. Updated with minor typographical corrections: June 30, 2025.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.285
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Engineering Social Justice and PeaceSame topicDigital Education and SocietyFrench-language works237,207