A Tale of Two Cities: Amazon HQ2 Negotiations in New York and Virginia
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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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".