CIRCLING THE DATA – LEARNING TO USE FEMINIST POSTSTRUCTURALIST DISCOURSE ANALYSIS
Bibliographic record
Abstract
New and novice qualitative researchers can be overwhelmed, and they may face uncertainty with the variety of available approaches when choosing their research methods. Feminist poststructuralist discourse analysis is a qualitative analysis method that has not been described extensively in the literature, despite variations in its application by different researchers. In this paper, the author will describe her use of these analytical methods for her doctoral research study. The aim of this paper is to provide researchers with an in-depth example of how feminist poststructuralist discourse analysis can be used in qualitative research. To begin, a brief description of the theoretical perspective, feminist poststructuralism, that informs feminist poststructuralist discourse analysis will be provided, followed by an outline of the analytical process that was used. Two examples of how the analysis methods were applied to the data will be shared. Strengths and challenges will be identified, and suggestions will be made for researchers who are interested in using this method of analysis. This first-person account contributes to improved understandings of discourse analysis generally, and feminist poststructuralist discourse analysis specifically. This account will be of particular interest to researchers new to discourse analysis or who are considering using feminist poststructuralist discourse analysis
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.086 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".