Blog: Belonging, Identity, Language, Diversity (BILD)/Langage, Identité, Diversité, Appartenance (LIDA). http://bild-lida.ca/blog/
Bibliographic record
Abstract
Launched in November 2014, the Belonging, Identity, Language, Diversity (BILD) / Langage, Identit e, Diversit e, Appartenance (LIDA) blog is published by the BILD Research Group, which began as a community of graduate students and faculty members in the Faculty of Education at McGill University in Montr eal, but now includes active and affiliate members from other universities in Qu ebec.In their brief introduction to the site, the members of the group articulate that their goal is 'to start conversations about local happenings and experiences in Montreal' and to 'write about the world around [them] from [their] own critical sociolinguistic perspective(s).'Published weekly and intended for academic and non-academic audiences, the site includes entries that are usually around 500 to 1000 words and can include photos, videos, or links to social media.While it can be written in any language or combination of languages, most entries are in English or French.It accepts guest bloggers, and provides full license in terms of genres and styles, publishing everything from brief, informal reflections, to poems and comic strips, and more academic-style essays.The BILD members review each other's posts, while a Blog Editor reviews and mentors guest bloggers.In the past five years, BILD has published upwards of 150 posts from researchers within Quebec, across Canada, and countries like Australia, Greece, the Netherlands, and the U.S.A.This goal of facilitating online conversation cannot be separated from the larger purpose of the BILD group, which founding member Mela Sarkar (22 December 2016) says was conceived as a democratic way of sharing information among 'a bottom-up, organic and uncontrived PLC [professional learning community].'The hope was that, through this PLC, junior scholars would be able to explore different ways of 'doing "being scholars."' Apart from engaging with peers and established
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.174 | 0.052 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".