Bibliographic record
Abstract
Open-access technologies cannot be denied their powerful impact on public knowledge and democratic education. These technologies include, but are not limited to, examples such as Google Earth, Wikipedia, Google Books, blogs, and open-access e-journals. Presently, open-access technologies permit students and educators the means to extend their education and enhance their democratic participation through open-access knowledge (Willinsky, 2002). Internet users can engage in educational activities previously unimaginable: students and educators can tour three-dimensional representations of natural wonders such as the Grand Canyon and Tanzania’s National Gombe Park (Google Earth); they can read any of Wikipedia’s 4.6 million entries (including entries for the two previously mentioned natural wonders); they can preview Aldo Leopold’s writings on Google Books (along with David Orr); they can consult blogs by famous environmentalists such as David Suzuki and the Goodall/Gombe Chimpanzee blog; and, finally, they can access e-journals such as First Monday (one of the first Internet peer-reviewed social science journals) and, in environmental education, the only open-access peerreviewed journal, the Canadian Journal of Environmental Education. Educators and educational researchers cannot ignore multimedia technologies’ powerful impact on youth culture and, as importantly, youth’s democratic take-up of these technological tools to voice their concerns, ideas, and cultural contributions. From the memorization of 300+ species of Pokemon (Blamford et al, 2002) for video games such as the Nintendo bestseller Pokemon Pearl (with one million copies sold in five days), to visualizations of environmental apocalypses and dystopias (Anime/Manga classics such as Nausicaa [Miyazaki, 1984/2005] and Green Legend Ran [Saga & Yamamoto, 1992]), to collections of local environmental data by youth (e.g., the long-standing Race Rocks project at the Lester B. Pearson College of the Pacific), to culture-jamming alternative video-clips on YouTube (e.g., “Keeping it Green!: Saving the Environment by Riding the Bus”), youth are culling and incorporating these new technologies into their social lives in critical, selective, inventive, active, and imaginative ways. Environmental education researchers may be great scholars, but they often resist rather than embrace digital open-access technologies: they are losing opportunities to communicate and advocate an environmental education agenda in the public realm, weak at entering the multimedia arena of political and image-based public discussions, and recalcitrant at enticing youth to participate in environmental education through popular cultural media forms. Open-access technologies are being missed or avoided by many
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".