Bibliographic record
Abstract
The pursuit of security and profit has become inextricably intertwined with the constant quest for control. Unless we are able to calculate and plot an indeterminate future, then we cannot actively manipulate and master Fortuna to avoid succumbing to the forces of fate. In order to transform all the uncertainties with which we are confronted to our advantage, such an ambition has become equated with the mitigation of risk. So strong is this appetite for control that it has elevated the discourse of risk to a hegemonic status in the organization of virtually every dimension of existence (Power 2004). In fact, risk management has even come to connote the fulfillment of moral responsibility (Baker 2000; de Goede 2005; Ewald 1991). As the ubiquity of risk discourse penetrates an ever expanding myriad of spaces, risk calculus becomes prized and promoted; thereby reaffirming the (Keynesian) view that ‘individuals, organizations, and societies have no choice but to organize in the face of uncertainty, to act “as if” they know the risks they face’ (Power 2007: 203). This sentiment echoes Ian Hacking’s (1990) observation that the (supposed) taming of uncertainty as a calculable risk has been pivotal in making the world appear ‘less capricious’ by granting a greater semblance of control over what would otherwise be considered chaotic and potentially dangerous. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.043 | 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".