Introduction

Ask nearly any philosopher about publishing, and you will hear variations on a theme: crowded submission queues, slow decisions, overburdened referees, prestige bottlenecks, and a pervasive sense that the incentives shaping the system are out of alignment with the goals of good scholarship. The question posed in a discussion at Daily Nous—whether philosophy’s journal problem can be captured in one number—invites a clarifying thought experiment: if you had to compress the problem to a single quantitative signal, what would you measure?

The appeal of a “one-number” view is not that it replaces nuance, but that it offers a dashboard light—an at-a-glance indicator that something is structurally off. In this essay, I sketch what such a number could be, how to estimate it, what it illuminates, what it risks obscuring, and how it can motivate reforms without collapsing the complexity of philosophical inquiry into mere metrics.

The Journal Problem in Brief

Philosophy’s journal ecosystem exhibits a familiar set of interlocking stressors:

• High submission pressure: Many more manuscripts are submitted than can be published, often driven by hiring and tenure incentives.
• Slow throughput: Bottlenecks at desk review, referee assignment, and revision cycles lead to long times-to-decision and time-to-publication.
• Referee scarcity: A small pool absorbs a disproportionate review burden; refusals cascade and delays compound.
• Prestige concentration: A few journals carry outsized reputational weight, amplifying congestion at the top.
• Opaque variability: Practices differ widely across journals and subfields, making planning difficult for authors, especially early-career scholars.

Each factor influences the others, producing a queuing problem rather than a mere scheduling glitch. That is precisely why the search for a single summary measure has traction: queue health often can be summarized by a small set of ratios.

One Number: The Load Ratio

A natural candidate for a single, system-level indicator is what we might call the Load Ratio:

Load Ratio (L) = Annual Submissions / Annual Publication Capacity

where “Annual Publication Capacity” means the total number of publishable article slots across the journals in focus during the year (not including book reviews, symposia introductions, or editorials, unless we explicitly want to treat them as displacing research slots).

Intuitively:

• If L ≈ 1, the system is roughly balanced: in a year, about as many papers are submitted as there are publication slots. Delays hinge mostly on editorial processes, not structural scarcity.
• If L > 1, the system is overloaded: there are more submissions than slots, so even under ideal processing, average rejection cycles, and fair distribution, the queue cannot clear without multiple rounds of rejection and resubmission somewhere else.
• If L ≫ 1, the system is severely overloaded: almost all authors must endure multiple rejections and long waits, even for publishable work, because aggregate capacity is far below demand.

In queuing-theoretic terms, L approximates the traffic intensity of the publication pipeline—how hard the system is being pushed relative to what it can process.

Estimating L in Practice

Estimating the Load Ratio requires only two inputs:

1. Submissions: Count the total number of unique manuscript submissions to the selected set of journals in a given year. Where double-submission is prohibited, this will overstate total unique manuscripts if authors resubmit elsewhere after rejection; that’s acceptable if the unit of analysis is journal workload. If we want the ratio for the field’s pipeline, we can instead estimate unique papers entering the system (e.g., dissertations, working papers, first submissions).
2. Publication Capacity: Count the total number of research article slots actually published in that year across those journals. For fairness, include online-first outputs if they effectively consume slots.

Example thought experiment: Suppose 40 core generalist and specialist journals collectively publish 1,200 research articles per year. If those journals receive 9,600 submissions annually, then L = 9,600 / 1,200 = 8. That is, eight submissions are competing per available slot across the system. Even if every paper ultimately finds a home, the queue must route through multiple decisions, creating predictable delays and rejection cascades.

What L Explains

The Load Ratio connects directly to familiar pain points:

• Time-to-decision: As L rises, editors must triage more aggressively, desk rejections increase, and referee assignment gets harder; delays become systemic rather than episodic.
• Referee burden: With higher L, the expected number of referee requests per active scholar increases, even if acceptance rates are held constant.
• Acceptance rates: Holding capacity fixed, acceptance rates must fall as submissions rise, heightening randomness in outcomes among papers of similar quality.
• Cascading congestion: Rejected manuscripts re-enter the system elsewhere, amplifying downstream submissions and further inflating L at other journals.
• Career risk concentration: Early-career scholars face extended timelines and stochastic outcomes, magnifying the gatekeeping power of a few venues.

In other words, a high L is not merely an inconvenience; it’s a structural property that drives many downstream effects we often treat as independent.

Limits of a Single Number

Any single-number summary is a blunt instrument. L has at least four important limitations:

• Heterogeneity across subfields: Some subfields have very different submission patterns, referee pools, and acceptance norms. A field-level L can hide subfield-specific overload.
• Journal prestige tiers: Capacity is not substitutable across tiers; a slot in a highly prestigious venue is not the same “resource” as a slot in a niche venue, at least for career incentives.
• Process variance: Two systems with the same L can behave very differently depending on desk-rejection rates, triage policies, and response-to-revision cycles.
• Quality distribution: L is indifferent to the distribution of manuscript quality; it tells us about pressure, not merit.

For these reasons, L should be treated as a signal, not a score. It tells us there is a queueing problem, but not yet where the levers are easiest to move.

Complementary Metrics

To restore nuance without losing clarity, pair L with a few lightweight companions:

• Median time-to-first-decision (TTD): Days from submission to initial decision. Indicates triage efficiency and referee responsiveness.
• Referee Strain Index (RSI): Estimated referee requests per active potential reviewer per year, divided by a sustainable target (e.g., 6–8 reviews/year). RSI > 1 signals overload.
• Resubmission churn (R): Median number of rejections before eventual acceptance for publishable papers. High R indicates cascading congestion and costly randomness.
• Prestige Concentration (PC): Share of “career-critical” venues’ capacity relative to total field capacity. High PC amplifies L at the top tier, even if field-level L is moderate.

Together, these form a minimal dashboard. L tells us about structural pressure; TTD and RSI tell us about process friction; R reveals cumulative wear on authors; PC shows incentive-induced bottlenecks.

Interpreting L as a Policy Lever

If L is high, there are only a few categories of remedy:

• Increase capacity: Add journal slots, launch new venues, expand issues, adopt continuous publishing, or create respected non-journal outlets recognized in evaluation.
• Reduce avoidable demand: Adjust incentive structures that induce excessive slicing of results, redundant submissions, or prestige-only targeting.
• Improve flow efficiency: Strengthen triage, promote structured abstracts, use taxonomies for routing, adopt editorial boards with active, accountable turnaround commitments.
• Distribute prestige: Normalize a broader set of reputable venues in hiring and tenure decisions, lowering PC and reducing pressure at a thin top tier.
• Share reviewing labor: Encourage reviewer commitments tied to submission privileges, recognize reviewing as a citable scholarly contribution, and adopt reviewer co-credit systems.

These interventions interact. For example, if departments credibly recognize quality in a wider array of journals, authors diversify their targets, lowering top-tier L and the stochasticity of outcomes. Similarly, when reviewers are credited and expectations are explicit, RSI can drop, dragging down TTD.

Practical Use-Cases

• For Departments: Track L and PC annually and align evaluation policies accordingly. If L is high and PC is severe, broaden recognized venues and weigh preprint impact, invited symposia, or open peer commentary.
• For Editors: Publish TTD and acceptance-rate dashboards, introduce structured desk-review criteria, and signal topical priorities to reduce mismatched submissions.
• For Authors: Use L and TTD to plan timelines realistically. Where possible, prioritize fit and turnaround over tier alone when career context allows.
• For the Field: Consider community-wide norms: e.g., a service compact tying annual submissions to a minimum reviewing commitment, thereby stabilizing RSI.

Why One Number Helps—And Why It Isn’t Enough

It is tempting to dismiss the search for a single measure as technocratic optimism. But L can perform a rhetorical and diagnostic function. Rhetorically, it helps communicate to non-specialists—university administrators, press officers, or interdisciplinary colleagues—why delays and rejections are structural, not simply a matter of “trying harder.” Diagnostically, it anchors conversations about which levers are feasible given local constraints.

Still, the “one-number” frame must not crowd out the qualitative values of philosophy. The best work is often exploratory, slow-cooked, and not immediately legible to committees. A healthy publication ecology fosters risk-taking, subfield diversity, and constructive dialogue. L should be used to create room for that ecology, not to rank, sort, or shame.

Conclusion

The idea that philosophy’s journal problem can be captured in one number is provocative by design. If we must choose such a number, the Load Ratio—submissions divided by capacity—captures the key structural pressure that drives delays, referee strain, rejection cascades, and prestige bottlenecks. It does not tell the whole story, but it tells an essential part of it, and it points directly to the families of solutions most likely to help.

A constructive path forward is to pair L with a small dashboard—time-to-decision, reviewer strain, resubmission churn, and prestige concentration—while reforming incentives and practices that push the system past its sustainable limits. With that combination of clarity and nuance, the field can transform a nagging frustration into actionable change.

For readers interested in the conversation that sparked this framing, see the discussion at Daily Nous, a hub for news and commentary about the profession of philosophy.