Bibliographic record
Abstract
Etzioni has recently proposed that the success of Internet enabled commerce is surprising due to what I label the ‘trust in strangers’ problem. In here responding to Etzioni, I argue that the ‘trust in strangers’ problem effectively dissolves once it is recognized that current manifestations of Internet commerce are not associated with high levels of anonymity, but rather, with high levels of surveillance. In doing so, I first outline how data capitalism and security considerations have contributed to Internet surveillance being close to ubiquitous. Following this, I differentiate between three types of surveillance – i.e., top down, bottom up, networked – that many people who digitally connect rely upon. In concluding, I emphasize my basic argument. Namely, that it is ‘trust in surveillance’, rather than ‘trust in strangers’, that supports current manifestations of commerce online.
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 | 0.025 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.018 | 0.029 |
| Insufficient payload (model declined to judge) | 0.004 | 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".