Intersectional Analysis of Perceptions and Attitudes Towards Energy Technologies
Bibliographic record
Abstract
This document reports on a study of the perceptions and attitudes towards energy technologies undertaken in six case study communities in France, Ireland, Italy, Spain and the United Kingdom. This exploration is conducted as part of a research project exploring the ‘human factor’ in the energy system, within which a complementary study of energy-related practices is also being prepared. Both of these studies are taking an intersectional approach to the analysis, recognising that people have multiple, interdependent, overlapping axes of social identity – this research is focusing on gender, socio-economic privilege and age. The purpose of the report is to move away from the dominating paradigm of treating people as uniquely rational decision-makers and introduce the very real social contexts through which they negotiate and understand their role within the energy system; with specific focus on their views on the energy technologies that comprise it. The underlying feelings, assumptions, associations and values held by the people who express them are very real influencing factors on the attitudes and perceptions of people hold. Subsequently, a report will be produced synthesising these two intersectional analyses along with a range of socio-economic, technical, market and policy analyses from the ENTRUST project. It is intended that this report will be updated over the remaining duration of the project, based on ongoing dialogue with the communities; continued reflexive analysis of the collected data; and insights from complementary outputs (not least those mentioned above) with an updated report envisaged for release in quarter one, 2018. The report is laid out into sections, with each one addressing a specific aspect of the work involved to produce this deliverable. Section 2 outlines the Methodology for this deliverable, exploring the philosophical and theoretical background to the research. It also details the strategies and design processes that guided the selection of specific research methods and techniques used for data collection and analysis. An important contribution to the methodology has been the consideration of ‘intersectionality’, which has enabled the research to move beyond the “single-axis analysis” taken elsewhere. Section 3 provides an overview of the communities comprising a description of each of the six case study communities involved and an outline of their relevance to the research. Section 4 presents the presents the results and findings of the research, and discusses their meaning in the context of the research aims and objectives. The final section concludes the report with a number of key findings that this research suggests contribute towards determining attitudes to specific energy technologies.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".