Bibliographic record
Abstract
As I indicated in the Introduction, I will begin my tour of four select security referents at the macro level and work my way down. I will do so for four reasons. The first is to help shake off any lingering anthropocentric biases that might skew the analysis were we to work in the opposite direction. Human security, of course, naturally invites an anthropocentric treatment; culture as I shall be discussing it is also largely a human concern; and the state is a human creation. Were we to get into the habit of putting people at the centre of our analysis, we might do so too readily precisely where it would be least appropriate. The second is that ecospheric security will be the least familiar concept of the four and for that reason might risk coming across as an afterthought if I were to treat it last. Third, and relatedly, given the unfamiliar style of analysis to which I aim to subject these referents, I see advantages in giving it its first rigorous test in a context in which it is least likely to grate, hoping thereby to cultivate a degree of comfort with it once I turn to referents that we are used to analyzing in less unconventional ways. Fourth, and most importantly, I will argue that the ecosphere must take priority as a security referent, and accordingly must condition our understanding of the others. This argument would be more difficult to make were I to put various carts before the horse.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".