Bibliographic record
Abstract
While it is possible to measure how much an economic capital is worth, at least in terms of monetary value, through the use of sophisticated econometric tools, while conditioning other factors, in comparison, measuring social capital is significantly more challenging and complex business. The complexity in measuring social capital relates to the fact it is an intangible concept, multidimensional and multivariate in nature. Further as discussed earlier, social capital lacks a unified definition and dimension. To make matters more problematic, social capital is not a static construct that can be easily captured and conditioned during measurement, but rather it is a moving target, difficult to capture, without resorting to some assumptions and conditioning during measurement. An exploration of the various ways in which researchers have measured social capital is critical to our understanding of the range of approaches and techniques available, to guide us when thinking about measuring social capital. This will also enable us to make informed decisions on the most relevant and appropriate approaches and level of measurement as per a variable or component of social capital. This Chapter presents some of the major approaches currently employed to measure social capital and the approaches used to achieve this endeavour. The Chapter describes with illustration, the dimensions taken by each approach.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".