Algorithmic IF … THEN rules and the conditions and consequences of power
Bibliographic record
Abstract
The introduction to this special issue suggests we need to develop ‘a greater understanding of what might be thought of as the social power of algorithms'. In this paper, ‘social power’ will be critically scrutinised through a study of the entanglement of algorithmic rules with contemporary video-based surveillance technologies. The paper will begin with an analysis of algorithmic ‘IF … THEN’ rules and the conditions (IF) and consequences (THEN) that need to be accomplished for an algorithm to be said to succeed. The work of achieving conditions and consequences demonstrates that the form of ‘power’ in focus is not solely attributable to the algorithm as such, but operates through distributed agency and can be noted as a network effect. That is, the conditions and consequences of algorithmic rules only come into being through the careful plaiting of relatively unstable associations of people, things, processes, documents and resources. From this we can say that power is not primarily social in the sense that algorithms alone create an impact on society, but social in the sense of power being derived through algorithmic associations. The paper argues that this kind of power is most clearly visible in moments of breakdown, failure or other forms of trouble, whereby algorithmic conditions and consequences are not met and the careful plaiting of associations has to be brought to the fore and examined. It is through such examinations that the associational dependencies more than the social power of algorithms are made apparent.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".