Bibliographic record
Abstract
I was tempted to entitle this brief tribute to my friend Harold Wells, "When did Mum become Mom?" Like most Anglo-Canadians of my vintage, and like the generations before me, I grew up calling my mother "Mum"—once 1 had advanced beyond the childhood form "Mummie." But about thirty or forty years ago, I began to notice that newspapers and other media, when they resorted to the familiar for mother, increasingly rendered it "Mom." Then I found that my own siblings were writing about our "Mom." The straw that broke the camel's back was when my mother herself began to sign her letters "Mom." Well, it's undoubtedly a small matter. Most people seem not to notice. But Thus—silently, imperceptibly—do cultures change. When more than 88 per cent of the television programs Canadians watch emanate directly from the empire on whose remote northern edge we exist, why should anyone be surprised when "centre" becomes "center," and "pianist" becomes "pianist," and "zed" becomes "zee," etc.? When the films, pop music, TV evangelism, soap operas, sitcoms, stand-up comedy, etc., etc. by which Canadians are "entertained" twenty-four hours a day are nearly all "Made in America;' why should there be any astonishment in the ranks when it is noticed that Canadian youth are not only dressing like Americans but speaking like them too?
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.064 | 0.025 |
| Scholarly communication | 0.030 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".