Bibliographic record
Abstract
This rejoinder responds to critiques of my original article on conspiracy thinking surrounding the 15-minute city (15MC), arguing conspiracy narratives, far from benign misunderstandings, undermine rational public discourse and planning legitimacy. In particular, I address Pitas’s call for greater evenhandedness, contending such an approach risks legitimizing falsehoods under the guise of neutrality. Drawing on concepts of both-sidesism, misinformation, disinformation, motivated reasoning, I defend the role of leisure scholars in upholding evidence-informed debate, even in emotionally charged contexts. I clarify that while emotion is a vital dimension of public life, it must not override scholarly rigour. With strategic clarity and epistemic humility, I assert leisure scholars must distinguish between not only good-faith critique and bad-faith conspiracy but also good will in actively resisting the spread of disinformation while safeguarding inclusive, credible, and democratic discourse in parks and recreation. Ultimately, I affirm the ethical and civic responsibilities of scholars in an era of widespread misinformation and declining public trust.
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 | 0.044 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.060 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.034 | 0.065 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".