A sociomaterial stance approach to geoenergy RD&D organising
Bibliographic record
Abstract
This paper explores the stance on immersive technology in research, development and demonstration (RD&D) organising activity. Drawing on a government-led RD&D project, comprising shallow and deep geothermal energy (Northern Ireland, Department for the Economy, 2023), we show how members attempt to remedy a significant and enduring impediment – being able to access Plant Earth’s subsurface and showcase the thermal dynamics of geoenergy heating and cooling – with immersive technology practice. More in particular, we adopt a stance perspective toward immersive technology practice in RD&D activity. Stances are distinctive attitudes held by various institutional members in relation and are associated with the manner in which individuals position themselves and evaluate a phenomenon (Du Bois, 2007). Stances are not just beliefs, but are adopted, held, and expressed in human action (Fayard et al., 2016). Building on the work of Ahuja & Lampert (2001) on institutional familiarity traps, we explore how the role of unfamiliarity with Planet Earth’s subsurface conditions stances, impacts reasoning and evaluation (Ahuja & Lampert, 2001; Fayard et al., 2016). In organising RD&D practice, we study the way that institutional members organise immersive technologies and couple those with materiality (Monteiro & Nicolini, 2015) to stimulate new ways of reasoning and evaluation – new stances. In this research, we adopt a sociomaterial approach, whereby materiality is viewed as inseparable from the social world, providing insight into “how the entangled sociomaterial practices of specific apparatuses enact boundaries with certain performative outcomes.” (Orlikowski & Scott, 2023: 8). A cyclical model for the interaction between implicit new world views and the accommodative stances are outlined. Suggestions for research on stances towards immersive technology and sociomaterial practice in RD&D activity are supplied.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".