Funds of (Difficult) Knowledge and the Affordances of Multimodality: The Case of Victor
Bibliographic record
Abstract
Drawing on semi-structured� interview� data,� this� paper� examines� one� man's� multimodal� engagement with the emotionally difficult aspects of his Chilean heritage. It builds on recent work (e.g., Marshall & Toohey, 2010) that has begun to unearth the intersection between funds of knowledge (Gonzalez, Moll, & Amanti, 2005), difficult knowledge (Britzman, 1998, 2000), multiliteracies (New London Group, 2000), and multimodality (Kress, 1997) in an attempt to call attention to the shifting nature of what is considered� about� difficult� knowledge,� and� to� the� role� of� multimodality� in� both� accessing� and� making� sense� of� the� difficult� in� one's� funds� of� knowledge.� The� analysis� reveals� that� young� people� might� be� purposefully kept away from punctuations on their community's� semiotic� chain� that� are� deemed� difficult� (e.g., images, documentaries) not only by schools, but also by family members for whom such punctuations invoke painful memories. The paper concludes with a call to teachers to be ever mindful of reproducing knowledge hierarchies in their classrooms, which may be partly mitigated by discussing the affordances� and� challenges� of� drawing� on� students'� funds� of� (difficult)� knowledge� with� families� and� communities.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.033 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".