Between a Flake and a Strident Bitch: Making 'IT' Count in the Academy. (Articles)
Bibliographic record
Abstract
In this paper, we address issues of (ac)countability in the context of reflexively critiquing how feminist qualitative research is conventionally understood within the mainstream academy. The concrete examples we give may be from different individuals' experiences, or from a composite of experiences; they may be fictional truths or perhaps even creative non-fiction. Our goal is analytical, to show how social processes work with regard to making qualitative work count in the academy. As actors in a play, cast as somewhere between a flake and a strident bitch, our characters in this paper take on experiences rather than have them. Our tale dramatizes the workings of power/knowledge/space regimes, as embodied in the experiences of the characters who talk and walk through this text. Listening to you, I sometimes envy the way you can articulate your experience. I find it so hard to articulate mine. It has been subtly unspeakable: beyond the capacity of my speech. Often I am reduced to silence or I babble, pointing to but not being able to put words on what is going on. How can I separate my politics from my/self? And why is it that when I try to communicate what it is I am thinking, I'm reduced to a personality type, some psychological disorder, or dehumanized in a way that creates a formidable chasm between the other speaker and me. I am really worked up over this paper now. All those imprudent, petty, little (and not so little) things my colleagues have done over the years have come flooding back to me today. And I am angry. I want to work through this anger constructively -- and creatively.In this paper, we address issues of accountability in the context of reflexively critiquing how feminist qualitative research is conventionally understood within the mainstream academy. The experiences of a feminist colleague, threatened with a legal suit by the male members of her department after informally presenting a chilly climate report, shape how we talk about these issues.(1) The concrete examples we give in this paper may be from different individuals' experiences, or from a composite of experiences; they may be fictional truths or perhaps even creative non-fiction.(2) Our data, if we can use the term, are not in a realist sense but rather are illustrative of a range of experiences. By using data in this way, our goal is analytical, to show how social processes work rather than to represent empirically a particular place or population of scholars. Some might call this approach polemical. For us, it is a way to poke at the boundaries of what we think and open up ways to communicate how power can be deployed in everyday settings within the academy that set parameters for (ac)counting (for) feminist qualitative research.As actors in a play, our characters in this paper take on experiences rather than have them. Thus, our story does not refer to or represent any one person, individual university, or single department. Indeed, one person might experience almost everything that is described here; another, only one; and still another might have a different set of experiences but reach the same set of conclusions. We hope, as our scenes (seens) unfold, there may perhaps be room for still more experiences, room for even more drama, room for more of (y)our own stories -- between the words, the exchanges, the lines. But, for now, for us, our tale dramatizes the workings of power/knowledge/space regimes, as embodied in the experiences of the characters who talk and walk through this text.Enough already, on with the story.* * *Feminist scholars' experiences in the academy often appear quite different from each other at first glance. Our first character below, for example, might have been hired within a context of recognizing methodological differences and divisions in her discipline. She might even have been hired to teach feminist theory and qualitative methods and told by those not sharing her perspective (either feminism or qualitative methods) that she was indeed welcome. …
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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 | 0.058 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".