“We're all stories in the end”: on the narratives that (un)make us
Bibliographic record
Abstract
Narratives, and the public discourses that they feed into, shape how we think about ourselves, the work we do and both shape and are reinforced through the institutions we are submerged in.Narratives allow us to construct our identity and find our place within our particular context (Bruner, 1996).In this sense, narratives are inextricable from power (De Fina & Georgakopoulou, 2011).Rather, narratives are imbued with the power relations from which they rise, who can tell a story, who can claim it, and how the story can be told.As De Fina and Georgakopoulou assert, narratives are often an exercise in power relations and serve to perpetuate social inequalities.Narratives hold a special place in the discourse practices that shape ideologies (Jacobs, 2000).The public discourses that shape our social life are often fed by narratives about how this work is done and who does this work.These narratives are expected to conform to a certain motif, which effectively dictates which voices get heard more often or louder than other voices (Blommaert, 2005;Ehrenhaus, 1993).In education, the discourse around the work done in schools is often in contention with discourses around accountability, or resource allocation.The articles in this issue of Curriculum Inquiry (CI) all touch upon this relationship between narratives, discourse and power, particularly within the context of the United States.This includes exploring how narratives shape the way we see ourselves as educators and as citizens (Hochman and Lamb), examining how these narratives impact the work happening inside the classroom (Sherry), and unpacking larger discourses around what knowledge is deemed critical to a course, how the content is selected and what this means in the larger context of high stakes testing and short course durations (Parker and Lo).These public discourses, which play out in the public space, through the media, political rhetoric and policy making, directly affect the work that happens within the walls of the school.The narratives used often shape how we think of practice or how we construct our social roles.In her piece exploring nostalgia as a "limiting cultural force" (p.132) on school librarians, teacher educators and school administrators, Jessica Hochman illustrates the ways in which public narratives about school librarians directly influence how the profession is regarded by both those who practice it and those with power to dismantle it.Using Boym's (2001) distinction between restorative and reflective nostalgias, Hochman's "School Library Nostalgias" analyses the ways in which the school librarian's role is misperceived by school leaders, often invisibilizing the work they do and falling into stereotypes about their CONTACT
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.047 |
| Scholarly communication | 0.026 | 0.041 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".