“No Face Can Be Approached With Empty Hands and Closed Home”: Literacy in the Post-Truth Era
Bibliographic record
Abstract
Education has a responsibility to respond to the threat of deteriorating democracies (DeLuca & Christou, 2016; Peters, 2017). The post-truth era is marked by an erosion of trust in public institutions and conflict in online spaces. The broad purpose of this research is to examine the ways in which the evolving post-truth era has the potential to redefine how people consume information and make decisions, and to explore the implications for democracy. A more specific purpose is to draw attention to the limits of current literacy pedagogy and to propose a literacy education that engages deeply with questions of intersubjectivity. I begin with a discussion of the evolution of literacy education policy and curriculum, looking at disjunction between research and practice. I demonstrate the ways current literacy and media literacy education is not simply outmoded, but also limited by neoliberal conceptions of rationality and individualism. Offering a counterpoint to the status quo, I work with Levinas’ (1969, 1989) conception of ethics to consider the importance of three affective dimensions of literacy. I illustrate the tensions between affective reactionism and non-intentional affectivity, enjoyment and its disruption as a premise for intersubjectivity and two manifestations of anger—moral and defensive. I conclude with a proposal for literacy education that furnishes a space for the intersubjective relation to emerge. This approach comprises an intentional focus on relationality, responsibility and affect.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.055 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| 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".