Plausibility Under Duress: Counter-Narrative, Suspicion and Folk Forensic Contra-Plotting
Bibliographic record
Abstract
How would it portend to analytical contextualization as well as specific theorization when instances where narrative kernels, once weaved into alternative epistemologies, make their way into, and become (re-)”plotted” on an inherently political platform, a session of state Parliament? Motivated by such an inquiry, the present multidisciplinary paper develops its theoretical argument by interrogating the notions of “counter-narrating” and “counter-narrative” cast on the intertwined conceptual landscape of forensics, tracking, and suspicion. The theoretical discussion is advanced further by developing the notions of productive suspicion and contra-plotting. On analytical level, the present chapter maintains that the narrative structure of some parliamentary discourses (presentations, Q&As) may operate much in the same manner as an anonymous forum thread or a reply chain in news’ commentaries. In undertaking this multidisciplinary theoretical discussion and analysis, the aim of this paper is to inform and expand the scholarship on counter-narratives and, in particular, to further solidify the conceptual aspects of the act, or practice, of counter-narrating.
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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.078 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".