Facts are Meaningless Unless You Care: Media Literacy Education on Conspiracy Theories
Bibliographic record
Abstract
The aim of this paper is to propose an antithesis to the overreliance on scientific facts and objectivity to counter mis-and disinformation in media literacy education.As an antithesis, my negative argument will not be sufficient to provide a solution by itself.However, through this, I hope to re-examine the role of literacy-participation in meaning-making-in this anomic time and prepare a ground for the synthesis.Although some may find the title a bit controversial, the main argument I would like to put forward in this paper is a simple one: facts that are separated from values are in themselves meaningless.For those who are familiar with the critique of facts-values dichotomy, this statement may even sound banal.However, in the supposedly "post-truth" world, temptation to re-stabilize the ground of facts is palpable.I say "supposedly" because the genre of texts associated with the phenomenon of post-truth like fake news and conspiracy theories are not new, but those issues are foregrounded in public discourses today and such foregrounding makes their presence more vividly felt.For instance, Mordechai Gordon proposed three virtues that he believes should be emphasized more in education to counter the post-truth condition, which include: (1) respect for evidence, (2) cautious skepticism, and (3) pragmatic openness.1 Respect for evidence is straightforwardly about looking at evidence and listening to experts' judgements.Cautious skepticism, according to Gordon, refers to "the ability to ask good questions, to not take ideas for granted even if they sound plausible, and to listen carefully to people who disagree with you so as to avoid the danger of confirmation bias," and pragmatic openness is "the willingness to modify our views when the evidence suggests that change is warranted."2 These three virtues, Gordon says, "are indispensable tools for citizens in democratic societies that need to be able to continuously differentiate between truths on the one hand and misinformation on the other." 3 While I resonate with Gordon's sense of urgency, I am concerned that these three virtues, all of which
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.053 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".