Stabilising collaborative consumer networks: how technological mediation shapes relational work
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it