“Paved with Good Intentions:” Best Practices in the Ethics of Track Two Interventions
Bibliographic record
Abstract
Abstract This article unpacks the development of key ideas and debates which surround the ethical issues of Track Two. It defines what is meant by ‘Track Two’ and discusses how ethics might best be applied in practice to these dialogues. The ethical dimensions of four key issues are explored: accountability; the basis on which third parties feel they are entitled to intervene; the problem of dealing with actors who have committed atrocities; and ethical questions surrounding secrecy or confidentiality which is often required. The article suggests several ways forward in terms of creating a mechanism to enable practitioners to assist each other with the challenges they face. The article takes the view that a ‘hard and fast’ set of ethics may not be appropriate for the field, as each intervention is quite different, but rather that a set of ‘reflective questions’ should be developed to help practitioners confront ethical issues.
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 | 0.182 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".