Bibliographic record
Abstract
The identifiability effect is the human propensity to have stronger emotions regarding identifiable individuals or groups than for abstract ones. The more information that is available about a person, the more likely this person’s situation will influence human decisionmaking. This human propensity has biased law and public policy against environmental and ecological protection because the putative economic victims of environmental regulation are usually easily identifiable workers that lose their jobs, while the beneficiaries—people who avoid a premature death from air or water pollution, people who would be saved by medicinal compounds available only in rare plant and animal species, and future generations not subjected to harmful changes in climate—are unidentifiable abstractions. More fundamentally, however, this identifiability bias has helped create structural biases in legal institutions against environmental and ecological protection. For example, the doctrine of standing creates a bias against unidentifiable victims of environmental wrongs, because of the obvious necessity of showing injury in fact to an identifiable party. Other legal concepts common to a liberal legal tradition also serve to protect the interests of individuals—identifiable individuals—against state action. This liberal conception of law underweights the rights of unidentifiable individuals that are often beneficiaries of state action. Importantly, this is not simply a variant of public choice theory. Many lawmaking decisions and institutions harbor biases against unidentifiable individuals that are not explainable in monetary terms. And importantly, this is not simply a variant of the availability heuristic. Identifiability is more subtle and lasting than sensationalist media accounts of spectacular events that serve as available heuristics. The identifiability bias in environmental law is based on a fundamental human instinct, not a monetary one, and affects deeper decisionmaking processes.
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 | 0.076 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".