A Tale of Two Cities: Amazon HQ2 Negotiations in New York and Virginia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In 2018, Amazon made a surprise announcement in its highly competitive selection of a second headquarters (HQ2), a project that drew 238 bids from cities in the United States, Canada, and Mexico. Instead of one location, the company picked two, selecting Long Island City, Queens in New York City and National Landing, a massive parcel of land directly across the Potomac River from Washington D.C. in Virginia. HQ2 represented one of the largest economic development projects of its kind in modern American history. Notably, while New York City’s selection ultimately failed amid sustained public opposition, the Virginia selection succeeded. The two negotiations are highly illustrative, demonstrating differences in how each region considered the tangible and intangible interests of their counterparts, approached the need for political and genuine stakeholder engagement, and decided whether or not to employ a “Decide-Announce-Defend Approach.” While negotiation case studies provide opportunities to explore high-stakes negotiations and derive insights, the case of the two Amazon HQ2 selections has the added, rare benefit of presenting two negotiations with the same target deal, pursued in tandem, with similar stakeholder groups. Against the backdrop of the more widely known story of the unsuccessful New York City selection, this case analysis explores the lesser-known National Landing negotiation and its implications for negotiators.
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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 | 0.001 |
| 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) | 0.000 | 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 it