New Media Review: <em>What I Want You to Know</em>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The recent withdrawal of US forces from Afghanistan has renewed questioning about the justification and merits of the 20-year Global War on Terror. This article reviews What I Want You to Know, a timely new documentary that provides a cathartic space for 13 American combat veterans to address such questions. In less than 90 minutes, veterans deconstruct years of political deceptions against the backdrop of traumatic overseas experiences. The documentary’s strengths include its elevation of veteran voices, as well as its utility as an instrument of critical consciousness that challenges the political contexts for war. Limitations include the need for greater diversity of participants from additional military branches, ranks, and sociodemographic backgrounds, as well as the need for greater attention to resources for the transition home. Despite these issues, this film will appeal to war documentary enthusiasts and academics alike. It will prove particularly useful as a pedagogical tool in the classroom for university departments with coursework related to the study of moral injury and military-connected populations.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it