The Circles of Connections
Bibliographic record
Abstract
In modernity, there is a growing obsession with tracking various aspects of an individual’s life, that is the ‘quantified self’. The latest trends in technology have made it much easier to track many elements of life such as heart rate, weight loss, fitness activity, and sleep patterns. The list can be extended by collecting data on others as well (such as a baby or pet), leading to the notion of the ‘quantified other’. This new wave in quantified self/other data has an impact on social and behavioural science research as well, moving the field away from a focus on survey studies towards more complex data-driven approaches. However, feasible ways of measuring the more intangible aspects of life such as connectedness, feelings, and resilience are rarely on offer in the self-quantified market. To address this, in partnership with Red Cross Australia, we have developed a social visualisation tool that helps people to assess their social connections, and understand how these connections contribute to aspects of social capital such as participation, support, feelings of safety and trust. We believe having such a tool to self-quantify an individual’s social connections offers the potential for better public health outcomes. The greater impact can be made at a community level to understand and facilitate social connections of diverse communities and raise awareness about their needs. Enriching such information with other spatial or sociodemographic data can help organisations like the Red Cross for provision of targeted supports particularly around areas of disaster management and engaging marginalised or vulnerable populations, and thus to build more resilient communities.
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.
How this classification was reachedexpand
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".