Impact of Arctic Internet Connectivity in Ulukhaktok, Northwest Territories, Canada, 2022-2023
Bibliographic record
Abstract
This dataset includes transcripts of interviews with individuals from the community of Ulukhaktok, focused on exploring experience with Internet within the community. Topics included the current state of Internet access within the community, how individuals used the Internet, socio-cultural impacts associated with increased access to high quality Internet within the community, and ideas for future projects that leveraged improved Internet access. The dataset includes two different sets of interviews, one from the first field trip (summer 2022) and another from the second fieldtrip (summer 2023) of the project. Between these two trips, the community gained access to Internet via the Starlink satellite system. Interviews were semi-structured, conducted in English, and then later transcribed using the automated service OtterAI. Transcripts may contain errors associated with the automated transcription process, and a new set of cleaned transcripts will be uploaded to the Arctic Data Center when available. The transcripts have been de-identified to protect the identities of participants, following Institutional Review Board (IRB) protocol at the University of Washington. These data are also archived via Dryad, with the assigned identifier doi:10.5061/dryad.cjsxksnd8 .
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".