Bibliographic record
Abstract
“The Case for Quantitative Literacy ” substantially advances our thinking in at least four ways. It identifies various components (“elements”) of this style of thinking that together give us a comprehensive and appro-priately complex definition of quantitative literacy. It then gives a multitude of examples of actions and behaviors (“expressions”) occur-ring in daily life that call for this kind of thinking, from the simple to the esoteric. It next distinguishes the bundle of skills that constitute quanti-tative literacy as an academic subject. And finally, the case statement makes clear that quantitative literacy and mathematics are really two quite different things. I wish I had possessed such a precise and nuanced statement a quarter century ago when I wrote a Ph.D. dissertation in the field of American history about something I vaguely termed a quantitative mentality (Cohen, 1977). My use of the term mentality drew on the work of a French historical school prominent in the 1960s and 1970s (led by histo-rians Jacques LeGoff and Lucien Febvre) that championed the study of l’histoire des mentalités, meaning deep mental structures that persist in cultural groups over time. In contrast to the more typical historical focus on events, this kind of study explored the mental equipment, l’outillage mental, characteristic of a particular culture. The study of mentalité was sometimes thought of as the intellectual history of common people, the study of the thought patterns and fundamental attitudes of the members of a culture comprehended in the aggregate. But I found that mentalité was an inherently slippery concept to apply.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".