An open letter to the Universe: a reflection on conducting “good” research
Bibliographic record
Abstract
Purpose The purpose of this paper is to reveal a qualitative researcher’s journey into finding her sense of self during a trial she faced while conducting her dissertation research. Design/methodology/approach Indigenous research methodologies (IRM) mixed with an autoethnography were used. A critical reflexivity position, with respect to being in the field, was adopted, melding in the Universe, the Sun and the Earth as objects that the author can talk and interact with. This reflexivity was captured within the letter to the Universe. Findings Three outcomes are discussed. Notably, the implications of this work with respect to power-relations and gender. The issue of being in the field is then discussed. Finally, untangling the practical implications of using IRM/autoethnography as a combined method is presented. Social implications The letter to the Universe offers a guide of sorts to other qualitative researchers, via one person’s experience in the field. The letter is, in the end, a cautionary story for others, acknowledging that the author can respond to a trial in a gendered fashion, that one needs to be humble along with being persistent, flexible and resourceful toward achieving “good” research. Originality/value As a Western, White woman scholar, who circles Indigenous influences, the author demonstrated (through this letter) one possible way of embracing, and acknowledging, IRM without appropriating it.
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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 | 0.170 | 0.396 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.048 | 0.057 |
| Scholarly communication | 0.032 | 0.027 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.030 | 0.043 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".