Bibliographic record
Abstract
Purpose The purpose of this paper is to describe, situate and justify the use of creative nonfiction as an overlooked but legitimate source of text for use in social inquiry, specifically within the ambit of narrative inquiry. What potential lies in using creative writing, creative nonfiction specifically, as a source of text in social research? How may it be subjected to modes of analysis such that it deepens understandings of substantive issues? Links are explored between creative nonfiction and the social context of such accounts in an attempt to trace how writers embed general social processes in their narrative. Design/methodology/approach Three exemplars from literary magazines are described in which whiteness is the substantive theme. The first author is a woman who writes about her relationship with her landscaper, the second story is written by a man who is overwhelmed by guilt after uttering a racial slur, and the third text is by a man who describes his attempts to help a homeless couple. The authors’ interpersonal experiences with people unlike themselves tell something significant about the relationship between selfhood and power relations. Findings No singular pattern emerges when analyzing these three narratives through the critical lens of whiteness. This is because whiteness is not a subject position or static identity but a practice, something that it is done in relation to others. It is a collective capacity whose value is realized only in dynamic relationship with others. As a rich source of narratives, creative nonfiction may generate insights about whiteness and middle classness and how their intersections give rise to complex and contradictory sets of social relations. Originality/value There is very little precedence for using creative nonfiction as text for analysis in any discipline in the social sciences despite its accessibility, its richness and its absence of risk. Inviting the sociological imagination in its project to link the personal to the political, it opens possibilities for the analysis of both in relationship to each other. As a common form of narrating everyday understandings, creative nonfiction offers something unique and under-valued to the social researcher. For these reasons, the paper is highly original.
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 | 0.030 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.077 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| 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".