Bibliographic record
Abstract
This paper explores how Indigenous digital storytelling can be used as a mode for self-representation and decolonial learning in library makerspaces. Digital storytelling involves expressing your lived experiences and stories through a dynamic combination of textual and digital literacies. Implementing Indigenous digital storytelling programs allows library makerspaces to show the value of technology, digital and visual literacy, Indigenous Storytelling, and Ways of Knowing by letting Indigenous Peoples represent themselves and their lived experiences. This paper lays the groundwork on how library makerspaces can incorporate Indigenous approaches to digital storytelling. I argue that creating and implementing Indigenous-centered digital storytelling programs helps decolonize makerspace programming. Using integrative literature review methods, I will qualitatively identify the values of Indigenous Storytelling and digital storytelling to see how they interconnect. I examine how Indigenous Peoples have used digital storytelling and what libraries have done to support digital storytelling and Indigenous Storytelling to explore how these practices can be better adopted by library makerspaces.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".