Bibliographic record
Abstract
By Linda EdgecombeIt's pretty pathetic when a gal from Kelowna, B.C., admits I've had enough of the white stuff and it's only the middle of January.Not only have we had record snowfalls, we've reached record low temperatures.It hasn't been this cold since 1979.At least in 1979, wearing a full-length fur was not yet a criminal offence.So keeping things politically correct, I'll keep to my micro fibre jackets and layer, layer, layer… So, it's too cold to golf and too hot at home, actually the country song was the other way around.What do we do when the Christmas and New Year's celebrations wane, the bills start coming in, and you realize you should have purchased several thousand dollars of RRSPs just to keep your taxes within reason?We eat, of course, wear expandable pants and pray no one sees us as we shop at our local grocery and video stores wearing our kids' hat and mitt combinations.By the end of February, most of us have given up on our New Year's resolutions and are now planning a new program to start right after the Easter chocolate has been consumed.This is so depressing, it's almost humorous.To add to this mood, as I write this short article, the band Bread is playing on the radio.Now there's a pick-me-up.A very good friend once advised me, "If you insist on sitting in the outhouse of life, only stay as long as it's warm, then GET OUT!"
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.127 | 0.089 |
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".