This Sense of Place/ this Living Archive: Cocreative Digitization and First Nations Peoples Remembering
Bibliographic record
Abstract
In 2016, we organized digital storytelling workshops with First Nations 1 participants in Melbourne (Australia) to cocreatively “map” sites of historical significance through locative technologies. These digital memory maps allowed participants to share their oral stories about their relationships to different places with broader audiences through a cultural walking trail mobile app from both their individual and their collective perspectives. Functioning much like a museum tour guide in an outdoor setting, we named this app “Memory-scapes,” as it would feature First Nations people's memories of different places, allowing interested members of the public (tourists, students, and educators) to listen to and watch the digital stories as they physically walked the trail. We found that a cocreative archival framework for digitizing these oral histories supported our work with community. Through this project, we illustrate how First Nations people's knowledges are populating the archive in forms that place the control of content back in their hands. These community-driven archives reveal how new archival practices are shifting the media landscape of representational possibilities. While calling attention to the politics of representing place, we also question the emancipatory potential of digital technologies for First Nations people.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.019 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".