Bibliographic record
Abstract
With this new issue of the journal, the scholarly project of analyzing educational contexts and relationships through the prism of social well-being and the rights of people to lead decent lives continues.Here, the issue is not always about the total absence of education, or even any apparent weaknesses of the delivery of learning possibilities to both young and adult learners.Many times, as we have relayed in the pages of this publication more than once, the center of the story should be the effectiveness of educational platforms and relations that can actively respond to the learning needs of different populations around the world.Indeed, around the world, for although the needs of the local and the global are not necessarily the same, they are not necessarily detached from one another.Today, we surely live in a world that is continually globalizing where social, economic, political and cultural connections are mediated by the educational and at a related level, by the technological.As such, learning contexts located in diverse settings are not necessarily delinked from one another, and the idea of policy, programmatic or methodological borrowings are more common than ever.But so are also the learning needs expected by different populations which, even when they have to be geographically detached, shouldn't be necessarily disconnected in their qualitative constructions and dispensations.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.144 | 0.071 |
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".