Bibliographic record
Abstract
Toby Miller’s “Intellectuals” is prescient in interrogating the collapse of the fourth estate, the diminished role of the public intellectual in contemporary media and political environments, and the superficial agenda-setting and muck-raking so prevalent in the news media today. Written in 2005, this article could (and perhaps should) be interpreted as a meta-commentary on the academic’s responsibilities and limitations in the public sphere. For the academic to be a viable commentator and contributor, she must de-contextualize and de-historicize the event of the day, churning complex developments into simple narrative snippets ready-made for mass consumption. With the current 24 hour election “news” cycle recently focused on the missteps of public academics within democratic campaigns (from Harvard Professor Samantha Power’s now infamous comments re: Hillary Clinton to University of Chicago economist Austan Goolsbee and Harvard Professors Jeffrey Liebman and David Cutler embroiled in a Canadian NAFTA snafu), it appears only a matter of time before the public academic is stripped of its “public” moniker, relegated to the policy sidelines as the cycle churns out another “breaking news” story of sexual perversion, corruption, and hypocrisy.
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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.303 | 0.144 |
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".