Authenticity Scaffolding Across Temporal and Spatial Planes: The Scottish Ginaissance
Bibliographic record
Abstract
Over the last twenty years, gin production in Scotland has exploded. This rapid and recent growth in Scottish gin distillation raises interesting questions regarding how a new industry strives for authenticity and how firms founded at different times seek to define themselves in an evolving and increasingly contested market. Our research finds that the answers to both of these questions centre on the development of authenticity. We thus contribute to authenticity theory by addressing two major questions that have erstwhile been inadequately explored by existing literature: First, we assess how are different interpretations and meanings of authenticity enacted and performed? Second, we examine how and why these meanings change over time? The case study provides insights into the ways in which multiple meanings of authenticity, centred on places, historical myths and materiality, become socially constructed, repeatedly disrupted and institutionally memorialized over time. We label this polygonal framework of recursively defining, invoking and deconstructing authenticity meanings as ‘authenticity scaffolding’ – the process by which varied meanings of authenticity coexist or collide with each other over time. In developing this framework, this research aims to contribute to existing authenticity literature by delineating the micro-processes that drive the evocation of multiple meanings of authenticity and elucidating the ways in which these authenticity constructs evolve and adapt to changing industry trends over time.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.030 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".