ACTIVITY THEORY AND THE DESIGNATION OF VULNERABLE POPULATIONS AS TACTICAL TECHNICAL COMMUNICATORS: FEMALE VETERAN FARMERS AS KNOWLEDGE-MAKERS IN THE USE OF AGRICULTURE TOOLS
Bibliographic record
Abstract
ABSTRACTACTIVITY THEORY AND THE DESIGNATION OF VULNERABLE POPULATIONS AS TACTICAL TECHNICAL COMMUNICATORS: FEMALE VETERAN FARMERS AS KNOWLEDGE-MAKERS IN THE USE OF AGRICULTURE TOOLS H. Ellie Donodeo, M.S. George Mason University, 2024 Dissertation Director: Dr. Isidore Kafui Dorpenyo This research uses a multi-method design ethnography methodology combined with thematic analysis and an activity theory framework to empower female veteran farmers as knowledge-makers in the use of agriculture tools. I combine Miles Kimball’s call for tactical technical communicators integration into technical communications and Emma Rose’s request for methods to designate minoritized groups as knowledge-makers in the use of technologies to answer, “How do female veteran farmers’ everyday practices and means of doing contribute to knowledge of the use of agriculture tools?” I theorize that female veterans habitually use metis during military service, particularly in relation to tools not designed for their bodies, and carry that metis into agriculture and other masculine-dominated career fields. The combination of activity theory and thematic analysis provides the means to designate knowledge-makers and tactical technical communicators in the use of tools.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".