Decolonization of Digital Learning Spaces: It’s Not About Knowing More but Knowing Better
Bibliographic record
Abstract
Working alongside members of communities who are remote and/or marginalized from the dominant socio-economic powers, the long-term goal of the Decolonisation of Digital Learning Spaces project is to empower communities in choosing, adopting, developing, and/or appropriating culturally appropriate and sustainable digital learning technologies. Before we can co-envision useful options, however, we must first know what questions to ask and how to ask. It is necessary, therefore, to find appropriate, efficient, and innovative approaches to better understand community needs and values. This paper describes the preliminary planning of the research project in creating an international network of community members, activists, and researchers, and in identifying and testing methods for eliciting needs, values, and ways of understanding the world. Selected methods must allow the researchers to step outside their own pre-conceived understandings to avoid dominating or imposing meaning upon the participants’ understandings. In this presentation, we describe: a) the goals and concerns that were the impetus for the project, b) the nascent network, c) potential knowledge elicitation methods, and d) the repeated single-criterion card sort method as the first method that will be piloted. This deceptively simple method allows research participants to use their own words to express their conceptualizations thereby reducing the influence of the researcher upon participants’ mental model and values.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".