Finalizing a journal volume during a global pandemic / Finaliser un volume de journal durant une pandémie mondiale
Bibliographic record
Abstract
Where to begin? Since the beginning of the COVID-19 pandemic and the social restrictions that followed, our perceptions of and relationship to work have been shaken to their core. Indeed, we live in a society where consistent and constant production is part of our daily reality. However, the COVID-19 pandemic has acted as a mirror, showing us our obsession with productivity and exacerbating the dangers associated with a system that has been known to be dysfunctional for several decades: capitalism. The pandemic and what has followed have also resulted in the whole world living an experience of collective ambiguity. This experience of ambiguity is felt differently depending on our privileges, be they social, economic, political, or racial. Despite this ambiguity, our politicians across the political spectrum have continued to insist on the relaunching of the economy and incited the population to continue to produce in order to ultimately to save the capitalist system. Even at university, we continue to adapt—for good or bad—to this new reality that is supposedly “temporary”.
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 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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.078 | 0.042 |
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".