The Web of Identity: Selfhood and Belonging in Online Learning Networks
Bibliographic record
Abstract
I attended a pre-conference workshop for PhD students and had an opportunity to network with Etienne Wenger, Laura Czerniewicz (University of South Africa), and Chris Jones (OUUK). This was an enormously rich experience in which students from Denmark and the UK (including me) could discuss our PhD research and receive constructive criticism and feedback. As a result of this experience, I have made a contact with a professor from the OUNL who is willing to include our EdD students here at Athabasca University in similar workshops to be held in Europe. I am just starting to establish communications between the groups. \n \nAt the conference itself, I attended numerous presentations at the conference giving me some ideas and tools to use for social network analysis (SNA) here at AU, for example. I have several pages of notes from the conference that I can photocopy if needed by the committee. \n \nI presented my own paper as listed above. The full paper can be found on the conference website: http://www.lancs.ac.uk/fss/organisations/netlc/past/nlc2010/abstracts/Koole.html. I am attaching the paper and the PowerPoint presentation along with this document. \n \nI was offered some new directions for my research into identity formation in online networks: \n•\tDavid Carr – an American theorist who claims that our identity is highly influenced by our physicality (body). \n•\tDanah Boyd – a PhD student working on digital identity. I have just downloaded two of her papers and her master’s thesis. Her advisor from the OUNL suggested I contact her to share resources.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".