Bringing the “Missing Pillar” into Sustainable Development Goals: Towards Intersubjective Values-Based Indicators
Bibliographic record
Abstract
This paper argues that the need for a core “fourth pillar” of sustainability/sustainable development, as demanded in multiple arenas, can no longer be ignored on the grounds of intangibility. Different approaches to this vital but missing pillar (cultural-aesthetic, religious-spiritual, and political-institutional) find common ground in the area of ethical values. While values and aspects based on them are widely assumed to be intangible and immeasurable, we illustrate that it is possible to operationalize them in terms of measurable indicators when they are intersubjectively conceptualized within clearly defined practical contexts. The processes require contextual localization of items, which can nonetheless fit into a generalizable framework. This allows useful measurements to be made, and removes barriers to studying, tracking, comparing, evaluating and correlating values-related dimensions of sustainability. It is advocated that those involved in operationalizing sustainability (especially in the context of creating post-2015 Sustainable Development Goals), should explore the potential for developing indicators to capture some of its less tangible aspects, especially those concerned with ethical values.
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 | 0.062 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".