In-between Spaces: Unconventional Yet Essential Considerations for Defence and Security
Bibliographic record
Abstract
Dr. Adlakha-Hutcheon discussed dualities between obvious pairs such as defence and security; science and technology; and the physical and virtual worlds and questioned at what point does one become the other? Whether these were truly distinct or continuums with messy middles. Furthermore, it is necessary to understand the middle/liminal spaces between pairs in order to more effectively identify and address security threats. This is apparent when one takes the example of established/emerged and emerging technologies (AI and emergence of generative AI like Chat GPT). Technologies have different impact and implications based on the context of their use, for instance the extent of positive or negative disruption that ensues upon their use. Thus, to address complex problems, it is necessary to look for disruptors in “in-between” spaces. Received: 10-08-2024 Revised: 11-02-2024
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.045 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".