A Different Storytelling of Technology Education Curriculum Re-Visions: A Storytelling of Difference
Bibliographic record
Abstract
this paper is an attempt to open technology education curriculum re-visioning to different angles of vision by thinking about it as a form of storytelling. Over the past two decades there have been efforts "to understand curriculum work as a storytelling practice" (Gough, in press), and as a "collective story we tell our children about our past, our present, and our future" (Grumet, 1981, p. 115). Gough (1993) adds that curriculum narratives are not only collective but "selective" stories, and in the case of technology education the selection of technology stories have been articulated from a particular, relatively small, cultural community---industrial education/arts. In light of global restructuring with its different allegiances and arrangements of information, capital, time and space, bodies and geographies, and poststructuralism's skepticism of narrative authority, I would like to place into question both the adequacy of the selection of technology narratives to represent the study of technology in our current technologized/technocratized society, and the relevancy of these stories to meet the needs and interests of the diversity of students entering today's technology education classrooms. Although curricular changes from industrial education/arts to technology education have been viewed as constituting a paradigm shift (Clarke, 1989; Todd & Hutchinson, 1991), from my positioning as one of few women in this programme area and writing within feminist and poststructural leanings, the Patricia O'Riley is a doctoral candidate in Educational Policy & Leadership at The Ohio State University . She is completing her research in Vancouver, BC, Canada. -29possibilities for a generative re-visioning of technology education that creates space for difference appear to have bee...
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.012 | 0.028 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 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".