Bibliographic record
Abstract
O ne hundred issues.It's easy to say, but in addition to being a landmark number, it also implies a great deal.First of all, that Voices of Mexico is a valuable publication that has sustained itself over the years, with ups and downs, but that has kept its readers and managed to find new ones.It's also a number that invites drawing balance sheets and accepting new challenges.It means being critical and self-critical and learning from the comments and suggestions from all participants: editors, directors, and former directors, contributors, and above all, readers.More than three decades ago, the unam found that it needed to have a publication specifically for English-speakers, above all in the United States and Canada, as a response to the fast-paced regional economic integration process beginning at the time.The magazine was born in 1986 with one essential objective that is still valid today: offering information and analysis about Mexico, its economy, society, and po litics, to readers in our two neighboring countries.All this, with the aim of building bridges of communication that would make 100 Issues ofThe magazine was born in 1986 with one essential objective that is still valid today: offering information and analysis about Mexico, its economy, society, and po litics, to readers in our two neighboring countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.006 |
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".