Bibliographic record
Abstract
Welcome to our first Organization & Environment (O&E) Collaborative Guest Editorial! As O&E enters its second year of new directions and approaches, and in an effort to test the first of several innovative ideas suggested by our stakeholders, coeditors Alberto Aragon-Correa and Mark Starik have invited Professor Marie-France Turcotte of UQAM to join Mark in collaborating on this first O&E editorial of 2014. Marie-France, in general, contributes her decades-long interest and expertise in both sustainability management and collaboration to this effort and, specifically, offers several suggestions on one of this issue’s main subthemes—urgent academic sustainability management actions. Regarding that theme, actions to reverse a number of now-familiar but still critical unsustainability trends (Brown, 2011) appear to many of us, who have made careers in any of a wide array of sustainability-related professions, to be urgently needed. Earth’s human population continues to expand by more than 200,000 “new” individuals (net) each and every day, with nearly all of this increase occurring in developing countries. Global carbon emissions continue to grow by more than 2% each year, resulting in additional concentrations that, by the end of this decade, will be nearly 50% higher than preindustrial levels, triggering increases in sea levels, reductions of Arctic sea ice, and more violent weather events, among other negative environmental (and subsequent socioeconomic) effects. Differences in incomes within many countries, both developed and developing, have continued to increase, and a billion people still live in extreme poverty, with nearly all of them suffering from hunger and malnourishment. Human trafficking, illegal child labor, poor working conditions, and other social ills continue to contribute to an extremely low quality of life for millions of people worldwide. Rates of biodiversity loss are several orders of magnitude compared with their historical levels and do not appear to be decreasing with time. And, while some sustainability indicators, such as life span, infant mortality, and access to clean water, have shown positive signs in the recent past, many related to the sustainability factors of ocean acidification, desertification, deforestation, and worldwide violence do not. Numerous environmental and socioeconomic organizations, from the various entities within the United Nations, to a multitude of regional, national, and local public and private institutions, agencies, and programs have sounded these warnings for most of our adult lives, so much so that such lists have become, for some observers, little more than familiar litanies of worldwide bad news.
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 | 0.007 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.187 | 0.210 |
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".