Federal employees or rogue rangers: Sharing and resisting organizational authority through Twitter communication practices
Bibliographic record
Abstract
On 24 January 2017, the Trump administration tried to censor various science-related federal agencies, most notably the National Park Service. This case study presents the emergence of “alternative” National Park Service Twitter accounts that subverted the ban and explores how “rogue rangers” share in and resist organizational authority through communication practices we interpret as dis/attributing communicative action to various figures to do so. Through qualitative analysis of textual and non-textual data pertaining to the accounts, we demonstrate that organizational members create ambiguity through communicative dis/attribution to do and say more things than authorized, while maintaining a link to their organization, for it is as members that their actions and words are authoritative. The study concludes by theorizing three contributions to the literature on authority and resistance, in particular in the context of social media: (1) it shows that authority and resistance are at play even outside of conventional organizations, which conversely means that social media activity can display a level of organizationality; (2) it demonstrates that the communicative performance of authority and resistance rests on membership ambiguity; and (3) it extends current conversations on the communicative performance of authority by showing that the same practices can also perform resistance.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".