Against Epistemological Fascism: The (Self) Critique of the Criticals - A Reading of Paraskeva's Itinerant Curriculum Theory
Bibliographic record
Abstract
One of richest and most central aspects of curriculum studies is conflict—sometimes aggressive—between different epistemological perspectives within and beyond the same ideological turf. Looking to the history of the field since the turn of the nineteenth century, such conflict is quite visible, not only in the perpetual battles for control, but also in how such struggles actually constitute the very enzyme of the field. Such battles are not socially sanitized, and the struggle for the US curriculum field has been an ideological carnage against eugenics—exposing class, race and gender battles within, between and beyond dominant and counter-dominant traditions and crossing cultural, economic and political dynamics (Apple, 1990; Giroux, 1981; Kliebard, 1995; Schubert, 1986, Schubert, Lopez Schubert, Thomas, & Caroll, 2002; Pinar, 1975; Grumet, 1981, Miller, 2006, Watkins, 2001; Paraskeva, 2011). In this context, Paraskeva’s (2011; 2016a) most recent work pushes the struggle over the curriculum to another level by denouncing the field itself as the leading ideological locomotive of epistemicide. In a full-blast and comprehensive ‘self critique of curriculum studies’ and critical theorists, Paraskeva denounces both hegemonic and counter hegemonic theorists as players in such curriculum epistemicide. Maria Luiza Süssekind is a mother of two who writes from the South. As a Brazilian scholar, she works primarily with subjects related to social and cognitive justice within curriculum, educational policies for difference, and the marks of colonialism as epistemicides and patriarchalism within elementary public schools. Currently, Süssekind coordinates the Curriculum WG at ANPEd (Brazilian Association for post-graduation and research in Education). After teaching History at elementary schools for 12 years she achieved a Magister Scientia degree at UFRRJ (Rural Federal University of Rio de Janeiro) and Doctorate in Education at UERJ (State University of Rio de Janeiro) and since 2009 works as a professor at UNIRIO (Federal UNiversity of Rio de Janeiro State). With several articles and books published in Portuguese she has been investing in internationalization since 2011 when she did a postdoctoral study with William F. Pinar at UBC/Canada.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.068 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".