Bibliographic record
Abstract
<div class="buynow"><a title="Back issue of Monthly Review, March 2016 (Volume 67, Number 10)" href="http://monthlyreview.org/product/mr-067-10-2016-03/">buy this issue</a></div>Ellen Meiksins Wood, who died on January 14, was coeditor of <em>Monthly Review</em> with Harry Magdoff and Paul M. Sweezy from 1997 to 2000, and a major contributor to historical materialist thought in the late twentieth and early twenty-first century. Her parents were socialist refugees, members of the Jewish Labor Bund who came to the United States in 1941, after fleeing Latvia in the 1930s, when indigenous fascists came to power. Her mother worked for the Jewish Labor Committee in New York and her father for the United Nations. Ellen obtained her B.A. in Slavic languages at the University of California at Berkeley and went on to do graduate studies in political science at Berkeley, where she met and married Neal Wood, a professor in the department. From the late 1960s to the late 1990s, she taught political theory in the political science department at York University in Toronto.<p class="mrlink"><p class="mrpurchaselink"><a href="http://monthlyreview.org/index/volume-67-number-10" title="Vol. 67, No. 10: March 2016" target="_self">Click here to purchase a PDF version of this article at the <em>Monthly Review</em> website.</a></p>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".