Bibliographic record
Abstract
When Howard Zinn died on 27 January 2010, I immediately began reading on-line obituaries and tributes to the people’s historian whose radical example had inspired countless men and women to stand up, to march, to resist, and, if necessary, to go to jail. That afternoon, I went to the library where I checked out as many Howard Zinn books as I could carry. For the next couple of weeks, I read about the Civil Rights Movement, the labour movement, the wars against Vietnam, Laos, and Cambodia, the brutality of American imperialism, and the madness of neoliberalism. What struck me was the clarity of his vision and the clarity of his writing. As an activist, he knew right from wrong and, as a writer, he knew what he wanted to say and how he wanted to say it. He was never an academic poser and his writing was neither burdened by impenetrable theory nor cluttered with incomprehensible jargon. In a 1966 article published in the New York Times, he rejected the role of the disinterested scholar, instead defining himself as a historian-citizen. “In a world hungry for solutions, we ought to welcome the emergence of the historian as an activist-scholar, who thrusts himself and his works into the crazy mechanism of history, on behalf of values in which he deeply believes. This makes him a citizen in the ancient Athenian sense of the word.”1
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".