Mattering in the metaverse: Re-imagining inclusive futures of work with immersive platforms
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
Immersive platforms have become increasingly embedded in the workplace, leading to questions about their effects on the future of work. For immersive platforms to be successful, they need to be inclusive. The future of work with immersive platforms involves designing for inclusive purposes. This commentary proposes mattering, i.e., the experience of feeling seen, valued and significant to others, as a foundational concept to understand the future of work with immersive platforms. This commentary argues that mattering provides a rich lens to engage critically with immersive platforms and work and to make sense of their strategic implications. While immersive platforms promise co-presence, embodied interactions, and access, they may reproduce precarity, marginalization, and algorithmic forms of exclusion. This commentary questions how immersive platforms challenge and enable mattering at work, discussing how they can affect workers’ recognition, contributions, and sense of dignity in diverse virtual or hybrid organizational settings. The commentary calls for a design and research agenda on immersive platforms that are not only evaluated by pre-existing performance measures but also by their capacity to cultivate mattering. This agenda is important to strategic IS scholars and practitioners committed to shaping inclusive futures of work.
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 | 0.003 | 0.000 |
| 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 | 0.003 |
| 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