Shady Figures and Shifting Grounds for Re/Truthing: Channeling McLuhan’s Posthuman
Bibliographic record
Abstract
A dramatic shift in ground across the American political and social landscape is taking place, the kind that happens when a figure such as Trump conducts himself in the media, including by Twitter. In describing approaches to navigating a changing world through media, Marshall McLuhan employed the concept of figure/ground to evaluate media and their effects: a pursuit in honing perceptions. In curriculum theory, we employ figure/ground analysis to better recognize when a traditionally accepted humanist lens as figure has largely precluded recognition of a posthuman grounding that now thoroughly structures the conditions of the developed world’s existence (Sharon, 2014). How do we as educators address McLuhan’s prescient concerns over networked technologies and shifting media brought about by the electric age and now deployed by the likes of Trump, his administration, network news, and a disenfranchised American populace?
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.051 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".