Nature's Past Episode 017: Virtual Field Trips, Automobiles, and Global Commodity Chains
Bibliographic record
Abstract
Over the summer, the NiCHE New Scholars group organized a virtual environmental history workshop that invited graduate students from around the world to participate in two days of discussion and review of working papers on a variety of topics in environmental history. Students from Canada, the US, Britain, France, Japan, and Australia were connected using Skype, Google Groups, and a WordPress blog to review compelling new graduate research in environmental history. \n \nOne of the hallmarks of the workshop was the virtual field trip. Because field trips play such a prominent role in environmental history workshops and conferences, the New Scholars organizing committee wanted to somehow include a field trip component in the virtual workshop. Using a combination of the photo-sharing service, Picassa, Google Maps and Google Earth, the workshop participants created an impressive collaborative geo-tagged photo essay on the topic of the automobile and its impact on landscapes as a global commodity.Workshop participants were asked to upload and geo-tag photos of the impact of automobiles on their local environments and provide brief annotations and captions for each picture. Those images were then three-dimensionally mapped, using Google Earth, to allow each participant to virtually travel this global commodity chain through images of the impact of automobility in all of the participant countries and regions. \n \nOn this episode of the podcast we speak with some of the participants from this virtual environmental history field trip and ask them about their collaborative work on this project.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".