Bibliographic record
Abstract
Purpose The case studies were examined in the context of a lack of confidence in what constitutes truth and knowledge. Design/methodology/approach A case study design taken examining specific instances where the emergence of populist political tactics in an unfettered media world has undermined public belief in what counts as knowledge and to cast doubt on the validity of the idea of truth. Findings From the examples used, it was seen that not just scholarship, but scholars themselves, found themselves under attack when presenting views that, however rigorously reasoned and supported by research fact, were deemed unacceptable by the extreme political right. Practical implications The knowledge creation purpose of universities is under threat and “business as usual” as a response will not address that threat. Originality/value This calls into question the future of universities and their professoriates in a post-truth world and asks what the academy can do to adapt to continue serve the common good when knowledge gives way to the powerful influence of the evidence-free rhetorical sound bite in the formulation of public policy and public opinion.
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 | 0.022 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".