Bibliographic record
Abstract
A lot of what we understand to be trust is not trust; it is, instead, an active and conscious decision to feign trust. We call this ‘as-if’ trust. If trust involves taking on risks and vulnerabilities, as-if trust involves taking on surplus risks and vulnerabilities. People may decide to act as if they trust in many situations, even when they do not have sufficient warrant to trust – which is to say even when they do not trust. Likewise, people might decide to act as if they trust even when they have good reasons to actively distrust. The surplus risks of as-if trust may be worth taking in a number of different contexts and for many reasons. We argue that as-if trust is a concept that should be added to our theoretical, practical, and political vocabularies of trust and distrust. In doing so, we discuss the main reasons for someone to act as if they trust. We then show that the practice of as-if trust has been recognized by other scholars but treated as trust. In response, we clarify how as-if trust differs from trust, and we discuss the utility and ubiquity of as-if trust, especially in politics.
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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".