Sebastián Carassai.<i>The Argentine Silent Majority: Middle Classes, Politics, Violence, and Memory in the Seventies</i>.
Bibliographic record
Abstract
Sebastián Carassai’s premise is simple. We know that memory is constantly changing, and we are aware that time alters our perceptions of the past. How, then, do we as historians undertake the reproduction of memories and the multiple meanings made from events that are at ever greater distance from the present? Riffing on Eric Wolf’s notion that some peoples can be without history, in that their ability to document and record events may have been limited by material conditions, Carassai asserts that some pasts are without accurate reflection, particularly given that certain memories are so quickly mobilized within political platforms and critical distancings. Argentina in the 1970s, like many other parts of the world, experienced a tumultuous global economy, a rise in political radicalisms within both the Left and the Right, and an increasing loss of state autonomy due to Cold War interventions. Carassai argues that this period was additionally marked by a growing self-awareness among the middle class. He reasons that between military governments and the third administration of Juan Perón, economic stability and preservation of the rule of law depended on the middle class and the changeability of its political mindsets, thus encouraging the formation of a distinct class identity.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".