Bibliographic record
Abstract
Since the mid-2000s ‘The Para-Academic industry’ has gained momentum and become ‘big business’. The industry is led by private companies like the scientific publishers and assisted by many different stakeholders providing various services to researchers like storage and retrieval of scientific papers, OA repositories, SoMe for researchers and so on. The starting point of my presentation will be a discussion of how the introduction of ‘New Public Management’ has created the momentum for the Para-Academic industry and has made academic life easier in some ways, but it has also (mis-)lead (some) researchers to increasingly focusing on optimizing and gaming the regime. Researchers share authorships like never, paying little or no attention to ethical guidelines like the Vancouver Protocol. They create citation cartels. They set up artificial profiles on Twitter, Facebook and LinkedIn creating SoMe echo chambers to boost their ‘societal impact measures’. All of which takes time away from what they should be doing: Building, rearranging and extending the house of human knowledge.
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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.088 |
| Scholarly communication | 0.032 | 0.025 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 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".