Bibliographic record
Abstract
For most kids growing up in Windsor, Ontario, the intensely industrialized landscape of Detroit is easily seen from the Canadian side of the river but almost never visited, says writer Tim Lougheed.As one of those kids, Tim welcomed an invitation from the Institute for Journalism and Natural Resources to take an intensive tour of this challenging and controversial urban landscape.M any established industrial centers across the United States endured a rough introduction to the global economy during the second half of the twentieth century, but few matched the roller coaster ride of Detroit, Michigan.The city emerged from World War II as the triumphant forge for what President Franklin Delano Roosevelt dubbed the "great arsenal of democracy." 1 Factories that had once churned out passenger cars and their components now supplied the U.S. military with all manner of vehicles, weapons, and other equipment.By the 1950s the city's consumer economy had been restored, along with Detroit's status as the epicenter of North American automobile production and a host of thriving smokestack industries.But already an exodus had begun.The urban population peaked at more than 1.8 million in 1950. 2 Today's population sits at just over a third of that number, 3 a statistic that speaks to how far this urban landscape has been reshaped during the past few decades.Race riots, economic devastation, political corruption, municipal bankruptcy-Detroit has withstood some staggering socioeconomic blows, mirrored by severe environmental health concerns.4,5,6,7,8 Without visiting Detroit, it is easy to imagine a ruined metropolis, but even the most cursory inspection offers evidence of a remarkable resilience.Environmental and public health problems can still be readily found, but so too can testaments to a desire to move past this legacy and create something new.In the words of the city's official motto, Speramus meliora; resurget cineribus: We hope for better things; it will arise from the ashes.
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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".