Post-colonial science education: the challenge of negotiating researcher positioning
Bibliographic record
Abstract
In this paper, I describe a methodology that I employed, and resultant methods that I designed, to facilitate a Critical Discourse Analysis exploring perspectives on Western modern science as a school subject discipline in a given Caribbean context. Using specific themes from post-colonial theory, I sought to engage with some of the viewpoints presented by key stakeholders in secondary science education in the research setting. This paper reports on the methodological framework for the research study, illustrating some of the philosophical challenges encountered during the development of the research methods. Specifically, I discuss the tensions associated with the pursuit of researcher legitimacy, the use of ‘scientific’ methods of inquiry in a work that seeks to be decolonizing, and the challenge of stimulating conversation about a topic for which participants might not possess readily formulated opinions. My response to these challenges resulted in methods that utilized a unique adaptation of Stephenson's Q methodology. Although these tensions are far from being fully resolved, the approach outlined helps contribute to our understanding of the complexities of negotiation that can occur when conducting research of this nature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.399 | 0.313 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.024 | 0.093 |
| Scholarly communication | 0.028 | 0.025 |
| Open science | 0.007 | 0.029 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".