The Transnationalization of Truth: A Meditation on Sri Lanka and Honduras
Bibliographic record
Abstract
The present article is an elaboration of the text prepared for a lecture, delivered in London, England, on Tuesday, October 19, 2010, as part of the Centre for Transnational Legal Studies’ annual Transnational Justice Lecture series. The paper begins, in Section II, with general comments on a notion of “interactive diversity of knowledge” and how that connects up to a view about the nature of truth. Sections III and IV then present salient aspects of events in both Honduras and Sri Lanka over the last two years, with the coup d’ état of 28 June 2009, in Honduras and the bloody end to the civil war in Sri Lanka in spring 2009 as fulcrums of the narrative. In each case, emphasis is also placed on the establishment of truth-related commissions or panels in relation to each country. The paper ends with a discussion of three interconnected quandaries—the inside/outside quandary; the consistency and fairness quandary; and the timing quandary. The timing (or staging) quandary offers some provisional thinking on the sequencing of processes related to truth, justice and reconciliation, offering some reasons not to fuse truth-seeking processes with either criminal justice or reconciliation processes—with special reference to the Sri Lanka context.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".