Bibliographic record
Abstract
I n 1942, Canadian and ameriCan governments agreed on the necessity to increase Canada's administrative, scientific, and technical competence in the Arctic to match that of other Arctic nations, which in 1945, resulted in an act of parliament that created the Arctic Institute of North America (AINA) (Parkin, 1966).In 1984, AINA began publishing Arctic, a multidisciplinary, quarterly journal with a mandate to spread awareness and communicate information about the Arctic and the activities of the institute more widely (MacDonald, 2005).In 1987, AINA produced a document which outlined a new mission to recognize the significance of the information industry and to provide Northerners with accurate information.From this, a distinct newsletter, Information North, was published.In 1997, the newsletter was brought into Arctic as a separate non-peer reviewed essay section called InfoNorth (MacDonald, 2005).This essay presents the results of a data survey of InfoNorth to characterize the contents of the section and evaluate opportunities for building greater diversity in authorship and content.At present, essays in InfoNorth are usually received as unsolicited submissions and published at the discretion of the editor.This journal section does not include analytical research results that would require review; for example, the essays are descriptive pieces, including short observations, personal experiences, interviews, and histories of notable people.The essay section is published in each issue of the journal and includes four to five essays per year.The December issue is devoted to project descriptions by students who have been recipients of two AINA sponsored scholarships: the Jennifer Robinson Memorial Scholarship and the Lorraine Allison Memorial Scholarship.These essays are excluded from the survey since they are a requirement of their scholarship acceptance and limited to Canadian students.The survey will focus on author identity and topics covered.Author geographic location or the location of the institute with which they belong are provided as well as gender and whether the author(s) is Indigenous.The essay topics are described from general to specific and there are essays that cover multiple topics.The results show that essays are most frequently written by North American authors, more males than females, and relatively few Indigenous authors are represented.The outcome of the survey demonstrates gaps that could be filled by deliberately soliciting more contributions, particularly from Indigenous and international scholars working in the North.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".