Bibliographic record
Abstract
The ideas and papers in this volume primarily showcase the work of a group of new scholars who will lead the next generation of educational practise and inquiry. While the topics explored are critical issues, the ways in which these new scholars have chosen to address them illustrates the diversity of voice, venue and value that has led them to present their work. Education and what it means has entered a new era in which the primary focus on education for the sake of education is strained. An educational free-for-all, in the sense of a no-holds-barred fight, seems in place as competition for market share, effective branding exercises and movement towards a client-based delivery of educational services (on demand as demanded) has been fuelled and compounded by litigation, accreditation, transfer credits and matters of patents, copyrights, ownership and monopoly. The link between education and financial well-being has been co-opted as the key to personal success. Unfortunately, the degree pursuit, often called the “paper chase” has become competitive for learners seeking scholarships, awards and entry into graduate school. This transition indicates movement from becoming well educated to employability potential paralleling much institutional retooling and sustenance of enhanced reputation and fiscal viability.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".