Bibliographic record
Abstract
One of the least-known tangible assets of the Arctic Institute of North America (AINA) is its photographic collection, which includes images dating from the 1890s to the mid-twentieth century. These 4000 photographs - which reside, uncatalogued and rapidly deteriorating, in two file cabinets - are a valuable resource. The rich visual records contain important scientific information on all aspects of the Arctic environment, its geology, archaeology, geography, glaciology, and anthropology, as well as information on Arctic exploration and the cultural life of the indigenous people. One example of the richness of AINA's collection is the 142 photographs donated by Walter Wood, scientist and explorer, who was engaged in AINA's Snow Cornice Project of 1948 and the Icefield Ranges Research Project in the St. Elias Mountains from 1961 to 1972. Visual communication through photography, including motion pictures and television, has accelerated over the last half-century to a point difficult to imagine before this technological revolution. Today there is a growing recognition that photographs are more than an accessory to history's written text. They are an essential element in the very construction of that history, both written and oral. AINA's collection of photographs is no exception. AINA proposes to make the collection widely available by creating an electronic database. Each photograph will be researched and scanned into the computer using the University of Calgary's InMagic database software. The potential for the collection is enormous. Made available to the indigenous peoples of the North, it will help them to recall their own history. It will present information of value to anthropologists, archaeologists, professional historians, and scientists. Commercial and academic publications needing visual material will also be able to draw on AINA's photographic archive. Like other great archives worldwide, AINA's will be an immense asset, providing flexible access to a wealth of previously neglected information. ... An important aspect of the project will be to work with Native people who may have ties to the unidentified individuals and places depicted in so many of the photographs, in the hope that they can contribute memory culture to this rich visual heritage.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".