Bibliographic record
Abstract
Articles published in the International Journal on Homelessness are to the purpose of sharing knowledge to prevent and end homelessness.As such, we publish more than just primary research, we also include a variety of structured reviews, discussion articles that provide reflective content, as well as book reviews.Within research we are open to all methodologies and methods, as long as the content meets the purpose of our journal.As such, as Managing Editor I see a broad spectrum of quantitative and qualitative research.I wanted to offer a brief reflection on why many qualitative research articles fail to advance to publication and what researchers might consider doing to move towards excellence.The terms 'quantitative' and 'qualitative' denote the form of the data.One is generally numbers, the other generally text, although may include things like images, sounds, and other non-numerical data.Declaring one is conducting quantitative research provides no information regarding the actual methodology being followed, the methods of data collection and analysis, and how the findings might be connected to existing knowledge.Therefore, rigorous quantitative research needs: theory, methodology, and method.The same holds true for doing excellent qualitative research, yet I see this more frequently missed for qualitative articles.Specifically, declaring one is doing qualitative research provides little information about one's actual theory or paradigmatic perspective, methodology, and method, it simply informs the reader of the type of data they might expect.Unfortunately, I see often that the term 'qualitative research' is taken to present a (presumed) specific theoretical lens, a specific methodology, and a specific method.We receive many article submissions along the lines of, "This
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 | 0.030 | 0.148 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.041 | 0.021 |
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".