Bibliographic record
Abstract
With present financial concern and the increase of aging populations the French government has seen a way to support women in co-housing within buildings with moderate rents (HLM).This represents a significant saving, both for the women and the government as these women support one another with no medicalized need to the end of their lives. We investigated the charateristics, attributes and qualities for such successful co-housing as the model Babayagas House in Montreuil. It is extremely important to get a good grasp of the way people fit together. Many groups have tried similar endeavours in order to live more economically, yet few have lasted 12 years like the model housing in Montreuil. This is especially true in Canada. Friends have decided to share a house, and after three to five years it all falls apart. In all parts of the world similar attempts are being made. In Korea and China, it is usually wealthier people who get together. Retirement housing is expensive in Canada and often women who lost their spouse also lost additional income while finding themselves alone and struggling.This study is of a qualitative nature (Creswell & Poth, 2018). The outcome is an inventory questionnaire to be used for the selection of members of similar co-living arrangements. First we researched well-being questionnaires to identify a format that would best suit the targeted population.We then analyzed personal journals to uncover desirable characteristics. We also analyzed documents from the public domain pertaining to the housing arrangements as well as the House Charter, each member has to sign upon joining the Babayagas House.All categories were examined and emerging themes were used as items for the identification of relevant questions from an already existing well-being questionnaire.Questions were slightly modified for the convenience of an aging population.These questionnaires are further reviewed by people presently in retirement homes for annotations as regards their content and appropriateness.Findings show a number of characteristics that emerged from the data analysis which are deemed necessary for on-going harmonious co-living. It comprises 33 sections, from autonomy and responsibility to adherence to rules in an attempt to identify personal traits based on aspects that emerged from existing data, namely the participants journals and other documents through which these traits were deemed to be conducive to better co-living.Examples will be given. The results will be discussed in light of the findings of the analysis and also as they pertain to the annotated questionnaires from present residents in retirement homes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".