Bibliographic record
Abstract
Most Canadians today are familiar with the term “the Global Village”. It is a catch-phrase loaded with positive connotations of unity, familiarity, and humanity on an international scale. Through the modern miracle of electronic networks, most now believe that very personal linkages can be created between just about anyone in any part of the world, regardless of the distance. In short, it means that no-one need be a stranger anymore. However, this is a bit of an incomplete reading of what Marshall McLuhan meant when he originally coined the term. While to the modern urban-dweller a village rustles up images of tire swings, porch doors and the time to sit and talk about the “important things” of life, McLuhan would admonish this perspective as mere nostalgia from people who never actually lived in a village. To him, a village is more claustrophobic, people not as caring as one would think. Instead, it is a place where nosy parkers know your business, the crack in your drapes is a focal point for snoopy neighbors, and a non-stop stream of gossip is purveyed at the local post office. To McLuhan, life in the Global Village was a life lived vicariously. He did not condemn this, but just discussed this facet of the future with a sense of inevitability.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.024 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.037 | 0.012 |
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".