Bibliographic record
Abstract
Is now a good time to buy a home? It’s a question that thousands of Americans are asking. And consumers in DallasFort Worth aren’t finding an easy answer. After months of media reports about the nationwide housing slowdown, some potential homebuyers are on hiatus. Reports of falling prices in California and Florida have many buyers sitting on the fence. Local real estate agents have noticed a chill in the market. Third-quarter pre-owned home sales were down about 5 percent, with the biggest drop 15 percent—coming in September. Pre-owned home prices also fell last month by 3 percent. At the same time, sales of new homes were flat and builders reduced starts by 12 percent. Even though some buyers have backed away, there are no signs of the dreaded housing bubble in Dallas-Fort Worth. Unlike in hot markets on the East & West coasts, home prices in North Texas have risen by less than 5 percent annually in recent years. There’s not much froth in our market. Indeed, a report last month by the global Insight and National City Corp. said that Dallas has one of the most undervalued housing markets in the country. PMI Group, one of the country’s largest mortgage insurance firms, says Dallas is among the cities with the lowest risk of a home price shakedown. And a new nationwide study by Moody’s Economy.com says that no Texas cities are on the list of U.S. markets that will suffer home price declines in the months ahead. However, Moody’s wouldn’t release a ranking of cities expected to do well in housing or provide details about Texas cities. I’m not surprised. Many national analysts want to focus only on the bad news in the housing market.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.554 | 0.403 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".