An Analysis of the United Nations Conference on Sustainable Development (Rio +20) Discourse Using an Ability Expectation Lens
Bibliographic record
Abstract
The United Nations Conference on Sustainable Development (Rio +20) was hosted in Rio de Janeiro, Brazil, from June 20–22, 2012, 20 years after the first Earth Summit, with the intention of creating solutions to current global environmental issues. In this paper we present the results of an analysis of academic and newspaper articles that covered the Rio +20 summit, using an ability expectation lens. Articles were collected from academic databases such as EBSCO, as well as from newspapers such as the Globe and Mail (Canada) and China Daily; the articles collected were coded for ability expectations using an extensive list of codes which has been designed to identify ability expectations. Analysis of the discourse has revealed a number of ability expectations, such as the ability to produce and consume, the ability to work, and the ability to control. These ability expectations reveal what is seen as a necessary part of development. The opportunities and challenges which occur during development may be related to the expectations of the public, academic, private and not-for-profit sectors. The authors submit that in order to move forward towards universal sustainable development, it is critical to consider the ability expectations which are both explicitly and implicitly included in the Rio +20 discourse.
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".