Bibliographic record
Abstract
This article will explore the relation of search engines to the freedom they invoke in human subjects. Away from questions about the social impact of search engines and their ethical use, it shall investigate the influence of search engines on ethical subjectifications. The article will criticise the common critique that search engines should only deliver neutral and objective results to their users, where ‘neutral’ and ‘objective’ are defined as anti-subjective. On the contrary, it will argue that search engines are designed to deliver subjective results. A possible ethical critique starts therefore where they fail to do so. Due to reasons immanent to the technology, search engines are never subjective enough in their relevance decisions. Their results collide at the same time with what their users expect them to deliver. The article will show that, far from being a disadvantage, this disagreement between the users’ expectations and the search engines results is what triggers an ethical subjectification.
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 | 0.114 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.101 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.022 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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".