Bibliographic record
Abstract
Summary Motherhood brings exciting changes and challenges. All at once you experience changes to family dynamics, priorities, work-life balance, and your leisure. In the case of outdoor leisure participation, a mother’s ability to participate is largely influenced by the age of your child, your physical stamina, and emotional or psychological ability to handle, expect, plan, and adapt to your child’s and your own changing needs during participation. Specifically, this is true in activities that take place in remote, or backcountry settings as opposed to urban or near-urban settings. Accessibility is limited to moms who can take on (and financially afford) to carry their child on their back for long periods at a time. Few trail systems, that are not located inside conservation areas or parks, are designed with accessibility in mind. For any parent, this includes not being stroller friendly. Social media is abundant with “hacks” for the once outdoorsy moms to get back outside with their kids but, unless you have the resources and time, this often is not realistic until you have an independently mobile child. Moms looking to participate in outdoor leisure with infants are subject to extensive costs, planning, and high levels of fitness to be able to enjoy outdoor spaces and wellbeing benefits. In this case study, I offer a reflection of my experience trying to re-enter the world of back-country hiking with an infant. I consider the financial, physical, and psychological constraints faced when trying to “belong” in the outdoors, in this new phase of life. Information © CAB International 2024
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 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".