‘Fast hospitality’ and technology: Contemporaneous connections between ‘liquid’ and ‘solid’ in modern times
Bibliographic record
Abstract
This article explores the use of technology to provide hospitality at high speed. The requirements of economic competition and achieving profitability underpin the need for speed. Speed, in this article, is viewed as a function of Zygmunt Bauman’s notion of liquid modernity but also points to evidence of solid structures. Solidly positioned corporations set the pace of the speed agenda. Trade journal articles offer insight into the corporate-managed push for technology-driven speed. Themes are noted that relate to ‘fast hospitality’: an overarching concept that blends the desire for speed and liquid relations with the profit-seeking practices of ‘solidly’ entrenched corporate entities. The interplay between Bauman’s notions of solid and liquid is presented as one of contemporaneous connection rather than historical transition. Rather than representing a shift from the solid to the liquid – a notion more consistent with Bauman’s work – ‘fast hospitality’ would appear to be deployed in a way that weaves them together strategically. Such actions help corporations avoid uncertainty as well as address an absence of focus and clear direction: conditions typically associated with liquidity. ‘Fast hospitality’ is a function of solid organizational structures that aim to preserve a rationalized economic order, thus minimizing uncertainty, during liquid modern times.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.045 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".