How Whiteness Shapes Public Narratives: A Critical Discourse Analysis of Media and the NSBA Letter
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
Purpose: The purpose of this paper is to explore media's discursive strategies that shaped public narratives related to the National School Boards Association's (NSBA) 2021 letter to the Biden administration (“The Letter” hereafter) and the NSBA's response following media coverage. The Letter requested federal support to address the threats and violence toward educators and district leaders occurring largely at school board meetings. Research Method: We employed critical discourse analysis and a lens of possessive investment in whiteness to examine nearly 100 national and local news articles, which were primarily conservative-leaning, and archives related to The Letter. Findings: We highlight three themes among the discursive strategies of this data, which included the misrepresentation of the term domestic terrorism, the use of war-like metaphors, and the mischaracterization of parents. We also described how these strategies likely contributed to the public backlash of the NSBA, including a declining membership, and the organization's attempt at damage control through public apologies, organizational changes, and a shift in its equity-oriented work related to racial justice. Implications for Research and Practice: These findings illustrate the importance of educational intermediary organizations and leaders to proactively engage with journalists, foreground their equity commitments in evidence-based research and expert consultation, and communicate beyond media. These findings also underscore the need for media to reconsider the content of their reporting, whose voices they emphasize and ignore, and the discursive strategies they adopt when reporting on educational issues.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it