Bibliographic record
Abstract
I came across Louis Helbig’s images on the web one Sunday morning, about two years ago. Sitting at my kitchen table, I called him up to express my admiration for the photographic work. Like Peter Gzowski’s old CBC Morningside radio show, he picked up the phone in his kitchen and we talked for a couple of hours, swapping perspectives on the political, cultural, and environmental issues swirling around the tar/oil sand developments. We hit it off, exchanging ideas about the beauty of industrial landscapes and the loss they also represented. We exchanged hope that his photographs might provoke a broader critical conversation about what was happening in northern Alberta. This interview took place in late 2011. Louis and his photographs had been discovered. We talked about the public reaction to his images of the tar sands, and we chatted about the direction his new work was taking. I hope that you share my delight with the sharp perspectives of this creative aerial photographer, who is passionate about nature, and questions the heavy footprint we are leaving on the Canadian landscape. Louis read over the Aurora transcript and selected some of his photographic images to illustrate his thoughts and responses to our questions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".