Stabilising collaborative consumer networks: how technological mediation shapes relational work
Bibliographic record
Abstract
Purpose This paper aims to examine the role of technology in shaping the interplay between intimate and economic relations in collaborative consumer networks (CCNs). Design/methodology/approach This research is based on a three-year participatory netnographic and ethnographic field study of hosts, guests and community members within the Airbnb home-sharing network in New Zealand. The data consist of interviews, online and offline participant observations and brief discussions onsite (large-scale Airbnb events, host meetups and during Airbnb stays). Findings The findings reveal how technologies shape the relational work of home-sharing between intimate and economic institutions through grooming, bundling, brokerage, buffering and social edgework. This paper proposes a framework of triadic relational work enacted by network actors, involving complex exchange structures. Research limitations/implications This study focusses on a single context – a market-mediated home-sharing platform. The findings may not apply to other contexts of economic and social exchanges. Practical implications The study reveals that the construction of specific relational packages by Airbnb hosts using their digital technologies pave a path for home-sharing to skirt the norms of the home as a place of intimacy and the market as a place for economics. This allows these two spheres to flourish with little controversy. Originality/value By augmenting Zelizer’s relational work, this study produces theoretical insights into the agentic role of technology in creating and stabilising a CCN.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".