MétaCan
Menu
Back to cohort
Record W4387092997 · doi:10.7202/1106317ar

On in-ness: What Cégep teaching keeps teaching me

2023· article· en· W4387092997 on OpenAlexaffvenue
Magdalena Olszanowski

Bibliographic record

VenueMcGill Journal of Education / Revue des sciences de l éducation de McGill · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsConcordia University
Fundersnot available
KeywordsReflection (computer programming)The artsOnline teachingMathematics educationSociologyPedagogyLiberal arts educationPsychologyHigher educationPolitical scienceVisual artsComputer scienceArtLaw

Abstract

fetched live from OpenAlex

A first-person essay on the ways that Cégep teaching is different from teaching at a university. The reflection explores how belonging — an "in-ness" — is enacted within a creative arts department by focusing on various experiences from being hired to navigating teaching online during the pandemic. By doing so, the author recognizes how they come to understand and promulgate belonging in the classroom.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.022
Scholarly communication0.0130.013
Open science0.0010.007
Research integrity0.0050.015
Insufficient payload (model declined to judge)0.0060.002

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 head0.331
GPT teacher head0.409
Teacher spread0.078 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueMcGill Journal of Education / Revue des sciences de l éducation de McGillSame topicArt Education and DevelopmentFrench-language works237,207