Bibliographic record
Abstract
In the spring of 2006, when Sir Paul McCartney came to New foundland to protest the seal hunt , the whole event seemed all too familiar. Hadn’t these battle-lines been drawn many times before? This division between an international class of celebrity do-gooders and a class of workers doing some god-awful job in the service of survival; this divide between a lobby of influential elites and a community of people in a remote part of the world - haven’t we seen all of this before? Here was a conflict replete with differences in politics, economics, culture and power over the media circus engendered by the battle. While the media focused on the obvious passions being inflamed by the event - the impoverishment in a rural Newfoundland community, the way the doomed seals tend to look just like the family pet or the way the celebrity (rock star, film actor, etc .) tends to look heroic as he or she apparently puts his or her own life at risk in the service of saving the world - I wondered more about what the staging of this conflict said about deeply perceived notions of enlightenment as they relate to one’s location in the world or simply to one’s place in Canada.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.107 | 0.075 |
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".